├── .gitignore ├── 3-layer-band_gap.txt ├── 3-layer-band_structure.txt ├── Bandstructure ├── Strucuture1.png ├── Strucuture10.png ├── Strucuture11.png ├── Strucuture12.png ├── Strucuture13.png ├── Strucuture14.png ├── Strucuture15.png ├── Strucuture16.png ├── Strucuture17.png ├── Strucuture18.png ├── Strucuture19.png ├── Strucuture2.png ├── Strucuture20.png ├── Strucuture21.png ├── Strucuture22.png ├── Strucuture23.png ├── Strucuture24.png ├── Strucuture25.png ├── Strucuture26.png ├── Strucuture27.png ├── Strucuture28.png ├── Strucuture29.png ├── Strucuture3.png ├── Strucuture30.png ├── Strucuture31.png ├── Strucuture32.png ├── Strucuture33.png ├── Strucuture34.png ├── Strucuture35.png ├── Strucuture36.png ├── Strucuture37.png ├── Strucuture38.png ├── Strucuture39.png ├── Strucuture4.png ├── Strucuture40.png ├── Strucuture5.png ├── Strucuture6.png ├── Strucuture7.png ├── Strucuture8.png └── Strucuture9.png ├── Bayesian_opt.py ├── Highlight_Figure.png ├── LICENSE ├── N_doped_EFF_max.txt ├── P_doped_EFF_max.txt ├── README.md ├── create-2layers.sh ├── create-3layers.sh ├── create-4layers.sh ├── predict_maxval.py ├── predict_structure.py ├── snl_prep.py └── xaxisvalue.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | -------------------------------------------------------------------------------- /3-layer-band_gap.txt: -------------------------------------------------------------------------------- 1 | 177 2 | Mo S Mo S Mo S 1.3958 3 | Mo S Mo S Mo Se 0.7432 4 | Mo S Mo S Mo Te 0.0 5 | Mo S Mo S W S 1.3541 6 | Mo S Mo S W Se 0.5214 7 | Mo S Mo S W Te 0.2659 8 | Mo S Mo Se Mo S 0.7526 9 | Mo S Mo Se Mo Se 0.5699 10 | Mo S Mo Se Mo Te 0.0 11 | Mo S Mo Se W S 0.5244 12 | Mo S Mo Se W Se 0.3602 13 | Mo S Mo Se W Te 0.0 14 | Mo S Mo Te Mo S 0.0 15 | Mo S Mo Te Mo Se 0.0 16 | Mo S Mo Te Mo Te 0.0 17 | Mo S Mo Te W S 0.0 18 | Mo S Mo Te W Se 0.0 19 | Mo S W S Mo S 1.3 20 | Mo S W S Mo Se 0.7327 21 | Mo S W S Mo Te 0.0 22 | Mo S W S W S 1.2705 23 | Mo S W S W Se 0.5124 24 | Mo S W Se Mo S 0.5292 25 | Mo S W Se Mo Se 0.3667 26 | Mo S W Se Mo Te 0.0 27 | Mo S W Se W S 0.5343 28 | Mo S W Se W Se 0.3379 29 | Mo S W Se W Te 0.0 30 | Mo S W Te Mo S 0.0 31 | Mo S W Te Mo Se 0.0 32 | Mo S W Te Mo Te 0.1609 33 | Mo S W Te W Se 0.0 34 | Mo S W Te W Te 0.0049 35 | Mo Se Mo S Mo S 0.7416 36 | Mo Se Mo S Mo Se 0.5882 37 | Mo Se Mo S Mo Te 0.0 38 | Mo Se Mo S W S 0.7444 39 | Mo Se Mo S W Se 0.3661 40 | Mo Se Mo S W Te 0.0 41 | Mo Se Mo Se Mo S 0.5713 42 | Mo Se Mo Se Mo Se 1.4193 43 | Mo Se Mo Se Mo Te 0.4862 44 | Mo Se Mo Se W S 0.9022 45 | Mo Se Mo Se W Se 1.2145 46 | Mo Se Mo Se W Te 0.404 47 | Mo Se Mo Te Mo S 0.0 48 | Mo Se Mo Te Mo Se 0.4903 49 | Mo Se Mo Te Mo Te 0.3239 50 | Mo Se Mo Te W S 0.0 51 | Mo Se Mo Te W Se 0.489 52 | Mo Se Mo Te W Te 0.1743 53 | Mo Se W S Mo S 0.7306 54 | Mo Se W S Mo Se 0.9197 55 | Mo Se W S Mo Te 0.0 56 | Mo Se W S W S 1.0952 57 | Mo Se W S W Te 0.0 58 | Mo Se W Se Mo S 0.3636 59 | Mo Se W Se Mo Se 1.2195 60 | Mo Se W Se Mo Te 0.4531 61 | Mo Se W Se W S 0.6912 62 | Mo Se W Se W Se 1.2001 63 | Mo Se W Se W Te 0.3835 64 | Mo Se W Te Mo S 0.0 65 | Mo Se W Te Mo Se 0.4189 66 | Mo Se W Te Mo Te 0.1732 67 | Mo Se W Te W S 0.0 68 | Mo Te Mo S Mo S 0.0 69 | Mo Te Mo S Mo Se 0.0 70 | Mo Te Mo S Mo Te 0.0 71 | Mo Te Mo S W S 0.0 72 | Mo Te Mo S W Se 0.0 73 | Mo Te Mo Se Mo S 0.0 74 | Mo Te Mo Se Mo Se 0.4817 75 | Mo Te Mo Se Mo Te 0.3603 76 | Mo Te Mo Se W S 0.0 77 | Mo Te Mo Se W Se 0.4904 78 | Mo Te Mo Se W Te 0.1915 79 | Mo Te Mo Te Mo S 0.2379 80 | Mo Te Mo Te Mo Se 0.3196 81 | Mo Te Mo Te Mo Te 1.0214 82 | Mo Te Mo Te W S 0.0 83 | Mo Te Mo Te W Se 0.5538 84 | Mo Te Mo Te W Te 0.8885 85 | Mo Te W S Mo S 0.0 86 | Mo Te W S Mo Se 0.0 87 | Mo Te W S W Se 0.0 88 | Mo Te W S W Te 0.0 89 | Mo Te W Se Mo S 0.0 90 | Mo Te W Se Mo Se 0.4531 91 | Mo Te W Se Mo Te 0.5861 92 | Mo Te W Se W Se 0.7345 93 | Mo Te W Se W Te 0.4032 94 | Mo Te W Te Mo S 0.0 95 | Mo Te W Te Mo Se 0.1683 96 | Mo Te W Te Mo Te 0.8947 97 | Mo Te W Te W S 0.0 98 | Mo Te W Te W Se 0.3982 99 | Mo Te W Te W Te 0.8605 100 | W S Mo S Mo S 1.3541 101 | W S Mo S Mo Se 0.7444 102 | W S Mo S Mo Te 0.0 103 | W S Mo S W S 1.3019 104 | W S Mo Se Mo S 0.7419 105 | W S Mo Se Mo Se 0.9022 106 | W S Mo Se Mo Te 0.0 107 | W S Mo Se W S 1.0953 108 | W S Mo Se W Se 0.6938 109 | W S Mo Te Mo S 0.0 110 | W S Mo Te Mo Se 0.0 111 | W S Mo Te Mo Te 0.0 112 | W S Mo Te W S 0.0 113 | W S Mo Te W Te 0.0 114 | W S W S Mo S 1.2736 115 | W S W S Mo Se 1.093 116 | W S W S W S 1.5627 117 | W S W S W Se 0.8662 118 | W S W Se Mo S 0.5343 119 | W S W Se Mo Se 0.6912 120 | W S W Se W Se 0.6675 121 | W S W Te Mo Se 0.0 122 | W S W Te Mo Te 0.0 123 | W S W Te W S 0.0 124 | W S W Te W Te 0.0 125 | W Se Mo S Mo S 0.5214 126 | W Se Mo S Mo Se 0.3661 127 | W Se Mo S Mo Te 0.0 128 | W Se Mo S W Se 0.3653 129 | W Se Mo Se Mo S 0.3594 130 | W Se Mo Se Mo Se 1.2145 131 | W Se Mo Se Mo Te 0.4912 132 | W Se Mo Se W S 0.6883 133 | W Se Mo Se W Se 1.2145 134 | W Se Mo Se W Te 0.4156 135 | W Se Mo Te Mo S 0.0 136 | W Se Mo Te Mo Se 0.4868 137 | W Se Mo Te Mo Te 0.5568 138 | W Se Mo Te W Se 0.7411 139 | W Se Mo Te W Te 0.401 140 | W Se W S Mo S 0.5122 141 | W Se W S Mo Te 0.0 142 | W Se W S W S 0.8662 143 | W Se W S W Se 0.6888 144 | W Se W Se Mo S 0.3379 145 | W Se W Se Mo Se 1.2001 146 | W Se W Se Mo Te 0.7372 147 | W Se W Se W S 0.6675 148 | W Se W Se W Se 1.5149 149 | W Se W Se W Te 0.6618 150 | W Se W Te Mo S 0.0 151 | W Se W Te Mo Te 0.3982 152 | W Se W Te W Se 0.6685 153 | W Se W Te W Te 0.359 154 | W Te Mo S Mo S 0.2659 155 | W Te Mo S Mo Se 0.0 156 | W Te Mo S W Te 0.0 157 | W Te Mo Se Mo S 0.0 158 | W Te Mo Se Mo Se 0.4137 159 | W Te Mo Se Mo Te 0.1915 160 | W Te Mo Se W Se 0.4171 161 | W Te Mo Se W Te 0.1833 162 | W Te Mo Te Mo Se 0.1715 163 | W Te Mo Te Mo Te 0.8876 164 | W Te Mo Te W S 0.0 165 | W Te Mo Te W Se 0.401 166 | W Te Mo Te W Te 0.8795 167 | W Te W S Mo Se 0.0 168 | W Te W S Mo Te 0.0 169 | W Te W S W Te 0.0 170 | W Te W Se Mo S 0.0 171 | W Te W Se Mo Se 0.3801 172 | W Te W Se Mo Te 0.4058 173 | W Te W Se W Se 0.6625 174 | W Te W Te Mo S 0.0049 175 | W Te W Te Mo Te 0.8607 176 | W Te W Te W S 0.0 177 | W Te W Te W Se 0.359 178 | W Te W Te W Te 1.0028 179 | -------------------------------------------------------------------------------- /3-layer-band_structure.txt: -------------------------------------------------------------------------------- 1 | 99 2 | Mo S Mo S Mo S 2.4949 2.4266 2.2568 1.9331 1.7677 1.7913 1.9379 1.9293 1.9563 1.9728 1.9728 1.9818 1.9806 1.9548 1.8916 1.7923 1.6687 1.5408 1.4377 1.3958 1.3958 1.5523 1.7951 1.7336 1.5463 1.5782 1.7946 2.1477 2.4029 2.4949 0.0 -0.0416 -0.183 -0.4451 -0.7871 -1.0792 -1.1912 -1.1041 -0.9465 -0.8828 -0.8828 -0.8176 -0.7261 -0.6545 -0.5935 -0.529 -0.453 -0.3686 -0.2942 -0.2615 -0.2615 -0.4037 -0.7001 -0.9471 -1.0605 -0.9413 -0.5871 -0.2492 -0.0562 0.0 3 | Mo S Mo S Mo Se 1.9154 1.8507 1.7248 1.5005 1.3626 1.3775 1.3732 1.3359 1.3371 1.3431 1.3431 1.3533 1.3586 1.3353 1.2678 1.1609 1.0297 0.8953 0.7872 0.7432 0.7432 0.9159 1.2363 1.3153 1.1437 1.1779 1.3776 1.6508 1.8289 1.9154 -0.3666 -0.4017 -0.508 -0.6576 -0.8507 -1.0239 -0.7705 -0.5826 -0.4751 -0.4414 -0.4414 -0.3917 -0.3272 -0.2826 -0.2457 -0.2026 -0.1469 -0.0823 -0.0249 0.0 0.0 -0.1098 -0.3421 -0.5253 -0.6468 -0.772 -0.711 -0.5473 -0.4136 -0.3666 4 | Mo S Mo S Mo Te 1.1448292 1.0915292 0.9837292 0.8993292 0.8061292 0.7439292 0.6186292 0.5447292 0.5129292 0.5053292 0.5053292 0.5134292 0.5160292 0.4840292 0.4070292 0.2936292 0.1587292 0.0218292 1.2777292 1.2428292 1.2428292 0.0555292 0.4204292 0.6927292 0.5581292 0.6120292 0.7905292 0.9243292 1.0726292 1.1448292 -0.4016708 -0.4218708 -0.5279708 -0.8258708 -0.5573708 -0.2565708 0.0147292 0.2039292 0.2958292 0.3197292 0.3197292 0.2361292 0.0573292 -0.0961708 -0.1767708 -0.1972708 -0.1786708 -0.1391708 -0.0871708 -0.0813708 -0.0813708 0.0507292 -0.3042708 -0.3237708 -0.2676708 -0.4416708 -0.6263708 -0.4916708 -0.4216708 -0.4016708 5 | Mo S Mo S W S 1.6944 1.6945 1.6945 1.6944 1.6942 1.6939 1.6938 1.6938 1.6937 1.6937 1.6937 1.6937 1.6938 1.6938 1.6939 1.694 1.6304 1.5006 1.3963 1.3541 1.3541 1.5151 1.6939 1.6935 1.5595 1.587 1.6943 1.6945 1.6945 1.6944 0.0 -0.0376 -0.1664 -0.4107 -0.7469 -1.0739 -1.2324 -1.1506 -0.9902 -0.9206 -0.9206 -0.8574 -0.7553 -0.6466 -0.5497 -0.4499 -0.3379 -0.2173 -0.1121 -0.0663 -0.0663 -0.2488 -0.5863 -0.8334 -0.9393 -0.8469 -0.5354 -0.2267 -0.0508 0.0 6 | Mo S Mo S W Se 1.6966 1.6328 1.5094 1.2906 1.159 1.1772 1.1574 1.1187 1.1182 1.1235 1.1235 1.1324 1.1401 1.1172 1.0495 0.9421 0.8103 0.6751 0.5661 0.5214 0.5214 0.6969 1.0214 1.1168 0.9456 0.9758 1.17 1.4368 1.6108 1.6966 -0.5173 -0.5411 -0.6125 -0.7407 -0.9543 -1.1791 -0.95 -0.7938 -0.7113 -0.6867 -0.6867 -0.6229 -0.5343 -0.4627 -0.3957 -0.3195 -0.2276 -0.1261 -0.0379 0.0 0.0 -0.1518 -0.4321 -0.6137 -0.7105 -0.8247 -0.7827 -0.6408 -0.5489 -0.5173 7 | Mo S Mo S W Te 1.5548 1.5026 1.406 1.3703 1.2427 1.0995 1.0317 0.954 0.9189 0.91 0.91 0.9172 0.9214 0.8903 0.8123 0.6973 0.5611 0.4233 0.3122 0.2659 0.2659 0.4541 0.8355 0.947 0.7358 1.014 1.2568 1.3514 1.4849 1.5548 -0.2928 -0.3351 -0.4905 -0.7745 -0.7338 -0.4392 -0.2005 -0.0622 -0.0103 0.0 0.0 -0.0933 -0.2814 -0.4245 -0.4808 -0.4703 -0.4161 -0.3392 -0.2678 -0.2366 -0.2366 -0.3602 -0.55 -0.552 -0.4493 -0.6011 -0.8266 -0.5448 -0.3494 -0.2928 8 | Mo S Mo Se Mo S 1.9241 1.8614 1.7565 1.572 1.4194 1.4246 1.3879 1.3489 1.3486 1.354 1.354 1.3623 1.3724 1.3525 1.2847 1.1759 1.0426 0.9064 0.7972 0.7526 0.7526 0.9297 1.2673 1.3557 1.1781 1.2222 1.4435 1.6985 1.8408 1.9241 -0.3426 -0.3719 -0.4681 -0.6319 -0.8425 -1.0264 -0.7728 -0.5844 -0.4766 -0.4427 -0.4427 -0.3932 -0.3287 -0.2839 -0.2466 -0.2032 -0.1472 -0.0824 -0.0249 0.0 0.0 -0.1099 -0.3423 -0.5255 -0.6469 -0.7693 -0.6961 -0.51 -0.382 -0.3426 9 | Mo S Mo Se Mo Se 1.816 1.7577 1.6602 1.5881 1.4658 1.4055 1.2878 1.2238 1.2009 1.1971 1.1971 1.2052 1.213 1.1873 1.1133 1.0001 0.8642 0.7263 0.6155 0.5699 0.5699 0.7533 1.1233 1.3796 1.2198 1.2615 1.469 1.6064 1.7389 1.816 -0.1934 -0.218 -0.3028 -0.4769 -0.7289 -0.957 -0.9141 -0.72 -0.6031 -0.565 -0.565 -0.4731 -0.3779 -0.3143 -0.2647 -0.2135 -0.1527 -0.0851 -0.0258 0.0 0.0 -0.1135 -0.3541 -0.5531 -0.683 -0.7393 -0.5569 -0.3416 -0.2265 -0.1934 10 | Mo S Mo Se Mo Te 1.05999704 1.00809704 0.91979704 0.92919704 0.86179704 0.68209704 0.53909704 0.44269704 0.39089704 0.37519704 0.37519704 0.37949704 0.37529704 0.33469704 0.25119704 0.13499704 -0.00010296 0.81809704 0.73409704 0.69939704 0.69939704 0.83649704 1.05569704 0.60589704 0.57569704 0.64189704 0.83369704 0.86879704 0.99059704 1.05999704 -0.51100296 -0.50430296 -0.55980296 -0.79360296 -0.68150296 -0.39730296 -0.13570296 0.05649704 0.15909704 0.18859704 0.18859704 0.12519704 -0.00790296 -0.11810296 -0.17260296 -0.18060296 -0.15640296 -0.11430296 -0.07300296 -0.05450296 -0.05450296 -0.10420296 0.28039704 -0.33240296 -0.32530296 -0.51380296 -0.63520296 -0.52440296 -0.49770296 -0.51100296 11 | Mo S Mo Se W S 1.6994 1.6379 1.5137 1.2935 1.1605 1.1779 1.1598 1.1213 1.1211 1.1264 1.1264 1.1357 1.1427 1.1195 1.0517 0.9445 0.8129 0.6779 0.569 0.5244 0.5244 0.6995 1.023 1.1156 0.9452 0.9767 1.1721 1.4409 1.6171 1.6994 -0.5063 -0.5315 -0.6066 -0.738 -0.953 -1.1814 -0.9523 -0.796 -0.7135 -0.6889 -0.6889 -0.6244 -0.535 -0.463 -0.3958 -0.3195 -0.2276 -0.1261 -0.0379 0.0 0.0 -0.1522 -0.433 -0.6147 -0.7115 -0.8249 -0.7808 -0.636 -0.5398 -0.5063 12 | Mo S Mo Se W Se 1.6088 1.5503 1.453 1.3861 1.2684 1.2001 1.0817 1.0167 0.9929 0.9887 0.9887 0.9968 1.0044 0.9783 0.904 0.7906 0.6546 0.5167 0.4058 0.3602 0.3602 0.5438 0.9149 1.1823 1.0234 1.0631 1.2681 1.3989 1.5307 1.6088 -0.3373 -0.3571 -0.432 -0.5982 -0.8542 -1.1175 -1.106 -0.9323 -0.82 -0.7829 -0.7829 -0.6843 -0.5741 -0.4851 -0.4073 -0.3253 -0.2308 -0.128 -0.0386 0.0 0.0 -0.1543 -0.4381 -0.6335 -0.744 -0.8097 -0.6692 -0.4679 -0.3642 -0.3373 13 | Mo S Mo Se W Te 0.98226186 0.93016186 0.84226186 0.85206186 0.78526186 0.60506186 0.46116186 0.36346186 0.31056186 0.29446186 0.29446186 0.29846186 0.29366186 0.25236186 0.16866186 0.05246186 -0.08233814 -0.09023814 -0.01733814 0.01446186 0.01446186 -0.11073814 0.19786186 0.52276186 0.49976186 0.56776186 0.75806186 0.79166186 0.91266186 0.98226186 -0.33893814 -0.37583814 -0.52663814 -0.84633814 -0.65033814 -0.36963814 -0.13363814 0.01566186 0.08176186 0.09806186 0.09806186 0.02606186 -0.11573814 -0.21743814 -0.25003814 -0.22853814 -0.16933814 -0.21783814 -0.32713814 -0.37283814 -0.37283814 -0.18613814 -0.30463814 -0.33193814 -0.28273814 -0.45773814 -0.67993814 -0.49423814 -0.37873814 -0.33893814 14 | Mo S Mo Te Mo S -0.41913105 -0.42823105 1.00736895 1.00346895 0.89186895 0.75936895 0.63256895 0.55656895 0.52286895 0.51456895 0.51456895 0.52146895 0.52566895 0.49516895 0.41776895 0.30306895 0.16686895 0.02916895 1.24926895 1.21466895 1.21466895 0.05766895 0.43216895 0.69606895 0.56416895 0.65786895 0.88146895 0.95606895 1.08106895 -0.41913105 -0.41913105 -0.48553105 -0.52203105 -0.81883105 -0.56703105 -0.26633105 0.00566895 0.19596895 0.28906895 0.31356895 0.31356895 0.22916895 0.04816895 -0.10763105 -0.18923105 -0.20993105 -0.19103105 -0.15133105 -0.08093105 -0.09333105 -0.09333105 0.05716895 -0.31373105 -0.33213105 -0.27603105 -0.44763105 -0.62763105 -0.48643105 -0.42713105 -0.41913105 15 | Mo S Mo Te Mo Se 1.07473779 1.02213779 0.93713779 0.95383779 0.87873779 0.69913779 0.55623779 0.45973779 0.40783779 0.39203779 0.39203779 0.39593779 0.39123779 0.35033779 0.26693779 0.15083779 0.01603779 0.81503779 0.73103779 0.69663779 0.69663779 0.83233779 1.06583779 0.61333779 0.58373779 0.66263779 0.86783779 0.88923779 1.00463779 1.07473779 -0.53036221 -0.51576221 -0.56426221 -0.80116221 -0.69206221 -0.40766221 -0.14506221 0.04873779 0.15303779 0.18323779 0.18323779 0.11923779 -0.01566221 -0.12776221 -0.18326221 -0.19156221 -0.16726221 -0.11816221 -0.08346221 -0.06516221 -0.06516221 -0.08996221 0.29053779 -0.33936221 -0.33336221 -0.52106221 -0.64026221 -0.52746221 -0.50926221 -0.53036221 16 | Mo S Mo Te Mo Te 1.30898909 1.26038909 1.18148909 1.19678909 1.14738909 0.95538909 0.79158909 0.66898909 0.59418909 0.56908909 0.56908909 0.56508909 0.54118909 0.48398909 0.39438909 0.27988909 0.15088909 0.02258909 1.19208909 1.17338909 1.17338909 1.22728909 0.42098909 0.73938909 0.78538909 0.95828909 1.11778909 1.13778909 1.24478909 1.30898909 -0.32611091 -0.37581091 -0.52191091 -0.68041091 -0.84251091 -0.58061091 -0.33161091 -0.13541091 -0.01781091 0.01978909 0.01978909 -0.01491091 -0.08401091 -0.13611091 -0.15591091 -0.14621091 -0.11301091 -0.06621091 -0.02211091 -0.00271091 -0.00271091 0.05188909 -0.24831091 -0.33191091 -0.38391091 -0.56321091 -0.56741091 -0.50881091 -0.39231091 -0.32611091 17 | Mo S Mo Te W S 0.2672196 0.1102196 0.4324196 0.2544196 -0.1640804 -0.2130804 0.0853196 -0.0024804 -0.1046804 -0.1392804 -0.1392804 -0.1350804 -0.1213804 -0.0222804 -0.0527804 -0.0028804 0.0691196 0.1297196 0.1807196 0.2017196 0.2017196 0.1232196 0.0201196 0.4159196 0.7700196 0.8541196 0.5572196 0.4776196 0.1052196 -0.0891804 -0.0891804 -0.3701804 -0.1826804 -0.6157804 -0.5303804 -0.5695804 -0.9228804 -1.0442804 -0.9655804 -0.8996804 -0.8996804 -0.9387804 -1.0053804 -0.0967804 -0.1321804 -0.1691804 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1.4488 1.343 1.3 1.3 1.4672 1.6404 1.6399 1.5612 1.5843 1.6408 1.641 1.641 1.6409 0.0 -0.0408 -0.1803 -0.4418 -0.7923 -1.1224 -1.2842 -1.2126 -1.0512 -0.9744 -0.9744 -0.9118 -0.8063 -0.6991 -0.6025 -0.503 -0.3912 -0.2709 -0.1659 -0.1201 -0.1201 -0.3024 -0.6396 -0.8866 -0.9923 -0.8973 -0.5738 -0.245 -0.0551 0.0 20 | Mo S W S Mo Se 1.1826 1.1826 1.1826 1.1824 1.182 1.1818 1.1816 1.1816 1.1816 1.1816 1.1816 1.1816 1.1816 1.1817 1.1817 1.1564 1.023 0.8867 0.7774 0.7327 0.7327 0.9102 1.1817 1.1814 1.1806 1.1814 1.1822 1.1825 1.1826 1.1826 -0.2657 -0.3068 -0.4421 -0.6485 -0.8509 -1.0247 -0.7712 -0.5832 -0.4756 -0.4418 -0.4418 -0.392 -0.3273 -0.2827 -0.2457 -0.2026 -0.1469 -0.0822 -0.0249 0.0 0.0 -0.1098 -0.3422 -0.5254 -0.6469 -0.7721 -0.71 -0.4989 -0.3209 -0.2657 21 | Mo S W S Mo Te 1.12774479 1.08374479 0.98474479 0.93894479 0.84794479 0.73484479 0.25844479 0.25844479 0.25854479 0.25854479 0.25854479 0.25854479 0.25854479 0.25854479 0.25854479 0.25854479 0.14664479 0.00824479 0.05894479 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0.9657 0.9657 0.9657 0.9657 0.9658 0.9658 0.9659 0.937 0.8033 0.6667 0.5572 0.5124 0.5124 0.6905 0.9658 0.9655 0.9649 0.9656 0.9663 0.9666 0.9667 0.9667 -0.4438 -0.4793 -0.5868 -0.7391 -0.9547 -1.1801 -0.9508 -0.7945 -0.7119 -0.6872 -0.6872 -0.6233 -0.5346 -0.4629 -0.3959 -0.3196 -0.2277 -0.1261 -0.0379 0.0 0.0 -0.1519 -0.4324 -0.614 -0.7109 -0.8251 -0.7831 -0.6262 -0.4913 -0.4438 24 | Mo S W Se Mo S 1.7027 1.6408 1.5354 1.3624 1.2322 1.2494 1.1688 1.1285 1.1271 1.132 1.132 1.1403 1.1506 1.1306 1.0625 0.9533 0.8197 0.6833 0.5739 0.5292 0.5292 0.7069 1.0491 1.1854 1.0056 1.0367 1.2419 1.4773 1.6194 1.7027 -0.481 -0.5038 -0.5803 -0.7256 -0.9501 -1.1795 -0.9502 -0.7937 -0.7111 -0.6864 -0.6864 -0.6231 -0.5348 -0.4632 -0.3962 -0.3198 -0.2278 -0.1262 -0.0379 0.0 0.0 -0.1518 -0.432 -0.6134 -0.7102 -0.8232 -0.774 -0.6137 -0.5116 -0.481 25 | Mo S W Se Mo Se 1.6155 1.5565 1.4591 1.3944 1.286 1.2052 1.087 1.0224 0.999 0.9949 0.9949 1.003 1.0108 0.9849 0.9108 0.7974 0.6613 0.5232 0.4124 0.3667 0.3667 0.5503 0.9217 1.1991 1.0383 1.0758 1.2789 1.4054 1.5376 1.6155 -0.3001 -0.3243 -0.411 -0.5894 -0.8525 -1.1176 -1.1013 -0.923 -0.8097 -0.7726 -0.7726 -0.6758 -0.5706 -0.4841 -0.407 -0.3251 -0.2307 -0.128 -0.0386 0.0 0.0 -0.1542 -0.4378 -0.6328 -0.7432 -0.809 -0.6643 -0.451 -0.3329 -0.3001 26 | Mo S W Se Mo Te 1.06204779 1.00984779 0.92244779 0.93474779 0.86724779 0.68634779 0.54184779 0.44364779 0.39034779 0.37404779 0.37404779 0.37864779 0.37514779 0.33524779 0.25234779 0.13634779 0.00154779 1.16984779 1.03944779 0.98644779 0.98644779 1.18274779 1.18424779 0.59524779 0.57804779 0.64974779 0.84944779 0.87624779 0.99374779 1.06204779 -0.51435221 -0.49445221 -0.55375221 -0.78635221 -0.67535221 -0.39185221 -0.13095221 0.06064779 0.16294779 0.19234779 0.19234779 0.12754779 -0.00895221 -0.12205221 -0.17765221 -0.18525221 -0.16015221 -0.11705221 -0.07525221 -0.05695221 -0.05695221 -0.10675221 0.27304779 -0.32975221 -0.32245221 -0.50905221 -0.64925221 -0.53095221 -0.49295221 -0.51435221 27 | Mo S W Se W S 0.9765 0.9766 0.9766 0.9764 0.976 0.9758 0.9756 0.9756 0.9756 0.9756 0.9756 0.9756 0.9757 0.9757 0.9757 0.9582 0.8247 0.6883 0.579 0.5343 0.5343 0.712 0.9757 0.9754 0.9751 0.9756 0.9762 0.9765 0.9766 0.9765 -0.418 -0.4489 -0.5488 -0.7167 -0.9484 -1.18 -0.9508 -0.7945 -0.7119 -0.6873 -0.6873 -0.624 -0.5356 -0.4638 -0.3965 -0.3199 -0.2278 -0.1262 -0.0379 0.0 0.0 -0.1519 -0.4323 -0.614 -0.7111 -0.8237 -0.7704 -0.5906 -0.4595 -0.418 28 | Mo S W Se W Se 1.5874 1.5306 1.4331 1.3711 1.2646 1.1792 1.0603 0.9949 0.9708 0.9664 0.9664 0.9744 0.9821 0.956 0.8817 0.7683 0.6323 0.4943 0.3835 0.3379 0.3379 0.5215 0.8935 1.1755 1.0162 1.0539 1.2564 1.3799 1.5123 1.5874 -0.2839 -0.3071 -0.3929 -0.5746 -0.8479 -1.1211 -1.1195 -0.9542 -0.8614 -0.8323 -0.8323 -0.7127 -0.5847 -0.4896 -0.4086 -0.3248 -0.2299 -0.1275 -0.0385 0.0 0.0 -0.1556 -0.4441 -0.6444 -0.7547 -0.811 -0.6532 -0.4334 -0.3154 -0.2839 29 | Mo S W Se W Te 0.9743002 0.9225002 0.8342002 0.8459002 0.7776002 0.5974002 0.4535002 0.3560002 0.3032002 0.2872002 0.2872002 0.2913002 0.2865002 0.2453002 0.1616002 0.0453002 -0.0896998 -0.0897998 -0.0168998 0.0150002 0.0150002 -0.1105998 0.1919002 0.5179002 0.4965002 0.5647002 0.7543002 0.7833002 0.9049002 0.9743002 -0.3375998 -0.3750998 -0.5262998 -0.8377998 -0.6513998 -0.3700998 -0.1333998 0.0164002 0.0828002 0.0992002 0.0992002 0.0271002 -0.1147998 -0.2166998 -0.2494998 -0.2280998 -0.1689998 -0.2253998 -0.3348998 -0.3807998 -0.3807998 -0.1931998 -0.3052998 -0.3324998 -0.2830998 -0.4599998 -0.6838998 -0.4965998 -0.3781998 -0.3375998 30 | Mo S W Te Mo S 1.1278184 1.0763184 0.9843184 0.9859184 0.8751184 0.7324184 0.6054184 0.5291184 0.4950184 0.4865184 0.4865184 0.4934184 0.4976184 0.4670184 0.3895184 0.2748184 0.1388184 0.0012184 -0.0296816 0.0014184 0.0014184 0.0322184 0.4112184 0.7044184 0.5679184 0.6407184 0.8611184 0.9311184 1.0587184 1.1278184 -0.1766816 -0.2260816 -0.4174816 -0.7722816 -0.4837816 -0.1881816 0.0513184 0.1900184 0.2421184 0.2526184 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0.8787 0.8545 0.8545 0.85 0.8256 0.7681 0.6785 0.5641 0.4352 0.3071 0.2039 0.1609 0.1609 0.3361 0.7039 0.6845 0.5632 0.8075 1.2979 1.4082 1.5162 1.5802 -0.0686 -0.1219 -0.29 -0.5692 -0.8974 -0.6591 -0.427 -0.2627 -0.1757 -0.1502 -0.1502 -0.1874 -0.2543 -0.2929 -0.2911 -0.2542 -0.189 -0.1075 -0.0327 0.0 0.0 -0.1273 -0.3291 -0.3958 -0.414 -0.5874 -0.6967 -0.363 -0.14 -0.0686 33 | Mo S W Te W Se 0.5850666 0.5513666 0.4370666 0.2382666 0.3114666 0.5349666 0.4143666 0.3596666 0.4015666 0.4283666 0.4283666 0.4397666 0.4383666 0.2869666 0.3634666 0.3593666 0.3422666 0.3283666 0.3123666 0.3008666 0.3008666 0.3512666 0.5258666 0.3765666 0.8633666 0.4419666 0.2670666 0.3968666 0.5417666 0.5850666 -0.1970334 -0.1566334 -0.1416334 0.0622666 -0.0200334 -0.3144334 -0.2077334 0.0125666 0.0894666 0.0945666 0.0945666 0.1010666 0.1073666 0.0672666 0.0540666 0.1341666 -0.2508334 -0.4192334 -0.4583334 -0.4553334 -0.4553334 -0.4783334 -0.2991334 -0.3919334 -0.1640334 -0.0585334 0.1789666 -0.0655334 -0.1460334 -0.1970334 34 | Mo S W Te W Te 1.3903 1.3432 1.2648 1.2787 1.236 1.0454 0.883 0.7615 0.6875 0.6628 0.6628 0.6572 0.6303 0.5709 0.4802 0.3653 0.2363 0.1081 0.0049 0.0331 0.0331 0.137 0.5053 0.8245 0.8455 1.0448 1.1998 1.2218 1.3281 1.3903 -0.2364 -0.2885 -0.4532 -0.7079 -0.9006 -0.641 -0.4108 -0.2479 -0.1617 -0.1365 -0.1365 -0.1715 -0.2333 -0.2673 -0.2623 -0.2235 -0.1572 -0.075 0.0 -0.0382 -0.0382 -0.0953 -0.2997 -0.3697 -0.3891 -0.5592 -0.6816 -0.5172 -0.3061 -0.2364 35 | Mo Se Mo S Mo Se 1.8332 1.777 1.6819 1.6031 1.4652 1.4233 1.3058 1.2421 1.2194 1.2157 1.2157 1.2236 1.2312 1.2054 1.1315 1.0184 0.8825 0.7446 0.6338 0.5882 0.5882 0.7715 1.1396 1.3767 1.2195 1.264 1.4779 1.6275 1.7588 1.8332 -0.2153 -0.252 -0.3679 -0.5447 -0.7525 -0.9638 -0.9045 -0.7128 -0.598 -0.5608 -0.5608 -0.4658 -0.3716 -0.3104 -0.2631 -0.2134 -0.1532 -0.0854 -0.0259 0.0 0.0 -0.1131 -0.3505 -0.5451 -0.6754 -0.7473 -0.6118 -0.416 -0.2646 -0.2153 36 | Mo Se Mo S Mo Te 1.0796986 1.0288986 0.9481986 0.9587986 0.8822986 0.7029986 0.5606986 0.4649986 0.4137986 0.3982986 0.3982986 0.4019986 0.3970986 0.3563986 0.2731986 0.1570986 0.0222986 0.8250986 0.7408986 0.7063986 0.7063986 0.8432986 1.0797986 0.6174986 0.5756986 0.6552986 0.8615986 0.9003986 1.0123986 1.0796986 -0.5345014 -0.5334014 -0.6054014 -0.8565014 -0.6962014 -0.4117014 -0.1491014 0.0445986 0.1487986 0.1788986 0.1788986 0.1153986 -0.0186014 -0.1302014 -0.1858014 -0.1945014 -0.1705014 -0.1125014 -0.0870014 -0.0687014 -0.0687014 -0.0838014 0.2957986 -0.3435014 -0.3378014 -0.5291014 -0.6685014 -0.5662014 -0.5305014 -0.5345014 37 | Mo Se Mo S W S 1.1869 1.1869 1.1869 1.1867 1.1864 1.1861 1.186 1.186 1.186 1.186 1.186 1.186 1.186 1.1861 1.1861 1.1679 1.0346 0.8983 0.7891 0.7444 0.7444 0.9215 1.1861 1.1858 1.1823 1.1858 1.1866 1.1869 1.1869 1.1869 -0.2938 -0.3375 -0.4783 -0.653 -0.8482 -1.027 -0.7736 -0.5854 -0.4776 -0.4438 -0.4438 -0.3934 -0.3283 -0.2833 -0.2461 -0.2029 -0.147 -0.0823 -0.0249 0.0 0.0 -0.1098 -0.3422 -0.5256 -0.6472 -0.7711 -0.7075 -0.5326 -0.3525 -0.2938 38 | Mo Se Mo S W Se 1.614 1.5571 1.4629 1.3926 1.2603 1.2042 1.0862 1.0218 0.9985 0.9945 0.9945 1.0024 1.0098 0.9838 0.9097 0.7964 0.6604 0.5225 0.4117 0.3661 0.3661 0.5494 0.9184 1.167 1.0099 1.054 1.2672 1.4087 1.5386 1.614 -0.3523 -0.3833 -0.4755 -0.63 -0.866 -1.1245 -1.1048 -0.9403 -0.8249 -0.7874 -0.7874 -0.69 -0.5796 -0.488 -0.4094 -0.3268 -0.2317 -0.1285 -0.0388 0.0 0.0 -0.1547 -0.4383 -0.633 -0.7435 -0.8135 -0.6933 -0.5124 -0.3937 -0.3523 39 | Mo Se Mo S W Te 1.04839262 0.99979262 0.90549262 0.66469262 0.47709262 0.44979262 0.51339262 0.42049262 0.37989262 0.37019262 0.37019262 0.38099262 0.37689262 0.33719262 0.25529262 0.14019262 0.00609262 -0.03120738 -0.01770738 -0.05280738 -0.05280738 -0.05070738 0.27899262 0.38509262 0.23419262 0.30799262 0.53589262 0.83819262 0.98299262 1.04839262 -0.27820738 -0.32420738 -0.49970738 -0.85950738 -0.59290738 -0.31730738 -0.08870738 0.05279262 0.11359262 0.12809262 0.12809262 0.05849262 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-0.7592 -0.7768 -0.5821 -0.3456 -0.2112 -0.1739 44 | Mo Se Mo Se W Te 1.8501 1.7321 1.4841 1.2052 1.0156 0.9762 1.0387 0.9292 0.873 0.8566 0.8566 0.8676 0.8806 0.8684 0.8189 0.7365 0.6335 0.5267 0.4399 0.404 0.404 0.5464 0.8169 0.8904 0.7635 0.8515 1.0702 1.3806 1.6945 1.8501 -0.5309 -0.5283 -0.6258 -0.838 -0.8471 -0.5806 -0.3482 -0.1892 -0.1093 -0.0869 -0.0869 -0.1344 -0.2227 -0.2787 -0.2861 -0.2531 -0.1891 -0.1075 -0.0327 0.0 0.0 -0.127 -0.327 -0.3828 -0.3842 -0.5686 -0.7589 -0.6095 -0.5288 -0.5309 45 | Mo Se Mo Te Mo Se 1.9312 1.8144 1.5873 1.3682 1.1484 1.0558 1.1178 1.0153 0.9583 0.9419 0.9419 0.9517 0.9676 0.9594 0.9099 0.8258 0.7212 0.6134 0.5262 0.4903 0.4903 0.6352 0.9147 0.9106 0.7884 0.9538 1.2404 1.4988 1.779 1.9312 -0.5828 -0.6004 -0.6189 -0.819 -0.8647 -0.5961 -0.3433 -0.1482 -0.0351 0.0 0.0 -0.044 -0.1333 -0.2033 -0.2338 -0.2296 -0.1993 -0.154 -0.1107 -0.0916 -0.0916 -0.1747 -0.3318 -0.4034 -0.4403 -0.6315 -0.692 -0.5929 -0.5919 -0.5828 46 | Mo Se Mo Te Mo Te 1.83 1.7246 1.5234 1.4266 1.2637 1.1725 1.0555 0.9202 0.8402 0.814 0.814 0.8221 0.8309 0.8113 0.7529 0.664 0.5574 0.4488 0.3606 0.3239 0.3239 0.4737 0.7837 1.0173 0.9238 1.0798 1.3103 1.4434 1.6925 1.83 -0.3439 -0.3669 -0.4024 -0.5159 -0.7425 -0.7434 -0.4994 -0.2984 -0.17 -0.1267 -0.1267 -0.1403 -0.1652 -0.1801 -0.1776 -0.156 -0.1169 -0.0668 -0.0205 0.0 0.0 -0.0892 -0.2633 -0.3744 -0.4519 -0.58 -0.4767 -0.393 -0.3735 -0.3439 47 | Mo Se Mo Te W Se 1.1998 1.1999 1.2 1.1998 1.1699 1.0834 1.1236 1.0129 0.9563 0.94 0.94 0.9498 0.9658 0.9576 0.9084 0.8245 0.7199 0.612 0.5249 0.489 0.489 0.6339 0.9187 0.9422 0.8166 0.981 1.1996 1.1999 1.1999 1.1998 -0.5072 -0.5252 -0.5919 -0.799 -0.8656 -0.5966 -0.3434 -0.1482 -0.0351 0.0 0.0 -0.0444 -0.1345 -0.2052 -0.236 -0.232 -0.2017 -0.1563 -0.113 -0.0938 -0.0938 -0.177 -0.3343 -0.4056 -0.4417 -0.6335 -0.6961 -0.5854 -0.5303 -0.5072 48 | Mo Se Mo Te W Te 1.6814 1.5755 1.375 1.2783 1.122 1.0376 0.9075 0.7719 0.6915 0.6652 0.6652 0.6732 0.6817 0.6618 0.6032 0.5143 0.4078 0.2991 0.211 0.1743 0.1743 0.3243 0.6345 0.8884 0.8002 0.9399 1.163 1.2947 1.5428 1.6814 -0.4586 -0.4691 -0.4937 -0.6177 -0.8443 -0.846 -0.619 -0.4418 -0.3272 -0.2878 -0.2878 -0.2984 -0.3149 -0.3178 -0.2992 -0.2567 -0.1909 -0.1094 -0.0338 0.0 0.0 -0.131 -0.342 -0.4393 -0.4937 -0.6368 -0.6006 -0.493 -0.4708 -0.4586 49 | Mo Se W S Mo Se 1.1379 1.1379 1.1377 1.1374 1.137 1.1367 1.1366 1.1365 1.1366 1.1366 1.1366 1.1366 1.1366 1.1366 1.1367 1.1366 1.1366 1.1332 0.9848 0.9197 0.9197 1.1356 1.1365 1.1363 1.1361 1.1365 1.1372 1.1377 1.1379 1.1379 -0.0989 -0.1456 -0.2966 -0.5303 -0.7546 -0.9658 -0.9022 -0.7105 -0.5957 -0.5585 -0.5585 -0.4648 -0.3711 -0.3102 -0.2631 -0.2133 -0.1531 -0.0854 -0.0258 0.0 0.0 -0.1131 -0.3507 -0.5451 -0.6751 -0.7479 -0.6143 -0.361 -0.1617 -0.0989 50 | Mo Se W S Mo Te 1.19943718 1.15813718 1.10393718 1.08063718 0.95233718 0.92753718 0.86583718 0.23343718 0.23353718 0.23353718 0.23353718 0.23353718 0.23353718 0.23343718 0.23343718 0.23313718 0.22463718 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1.08067289 0.95477289 0.92457289 0.23277289 0.23277289 0.23287289 0.23287289 0.23287289 0.23287289 0.23287289 0.23277289 0.23267289 0.23237289 0.21837289 0.03887289 0.23347289 0.23337289 0.23337289 0.23387289 0.23247289 0.23257289 0.23247289 0.23287289 0.23357289 0.23427289 0.23487289 0.23507289 0.23507289 0.23497289 0.23447289 0.23387289 0.23327289 0.23287289 -0.18772711 -0.03642711 0.03087289 0.04747289 0.04747289 -0.02642711 -0.17292711 -0.27942711 -0.31512711 -0.29512711 -0.23642711 -0.15722711 -0.08402711 -0.05232711 -0.05232711 0.07237289 -0.36842711 -0.39342711 -0.34292711 -0.52192711 -0.76632711 -0.58202711 -0.45382711 -0.40632711 53 | Mo Se W Se Mo Se 1.7894 1.7894 1.7895 1.7826 1.5607 1.5445 1.6632 1.6702 1.642 1.6376 1.6376 1.6456 1.6588 1.6521 1.6091 1.533 1.4357 1.3345 1.2528 1.2195 1.2195 1.3491 1.5611 1.4824 1.3152 1.385 1.6354 1.7895 1.7895 1.7894 -0.1143 -0.1447 -0.2529 -0.4653 -0.7557 -1.0285 -1.1614 -1.05 -0.9303 -0.8901 -0.8901 -0.7241 -0.5976 -0.4985 -0.4139 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1.1373 1.0714 0.9084 0.7723 0.6915 0.665 0.665 0.673 0.6815 0.6614 0.6027 0.5136 0.4069 0.2981 0.2099 0.1732 0.1732 0.3232 0.6345 0.8986 0.8365 0.9624 1.1663 1.296 1.5446 1.6826 -0.4257 -0.4426 -0.4877 -0.6155 -0.8406 -0.8414 -0.6139 -0.4332 -0.3151 -0.2749 -0.2749 -0.286 -0.3043 -0.31 -0.2943 -0.254 -0.1896 -0.1089 -0.0336 0.0 0.0 -0.1303 -0.3403 -0.4374 -0.4913 -0.6328 -0.6029 -0.4943 -0.4476 -0.4257 59 | Mo Se W Te W S 1.20043245 1.15883245 1.10383245 1.15023245 1.04593245 0.92993245 0.23573245 0.23573245 0.23583245 0.23593245 0.23593245 0.23593245 0.23583245 0.23583245 0.23573245 0.23543245 0.22373245 0.04573245 0.23643245 0.23643245 0.23643245 0.23693245 0.23553245 0.23553245 0.23553245 0.23593245 0.23663245 0.23723245 0.23783245 0.23793245 0.23793245 0.23783245 0.23743245 0.23683245 0.23623245 0.23593245 -0.18846755 -0.03706755 0.03023245 0.04683245 0.04683245 -0.02766755 -0.17526755 -0.28246755 -0.31816755 -0.29796755 -0.23916755 -0.15986755 -0.08676755 -0.05506755 -0.05506755 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0.17164216 0.17164216 0.10814216 -0.02575784 -0.13735784 -0.19305784 -0.20195784 -0.17805784 -0.09845784 -0.09475784 -0.07645784 -0.07645784 -0.06925784 -0.30575784 -0.35125784 -0.34565784 -0.53745784 -0.67765784 -0.57775784 -0.51925784 -0.48065784 63 | Mo Te Mo Se Mo Te 1.8641 1.7617 1.5712 1.4794 1.2856 1.1809 1.0905 0.9563 0.877 0.8511 0.8511 0.8588 0.8671 0.8473 0.789 0.7002 0.5937 0.485 0.3969 0.3603 0.3603 0.5096 0.8163 1.0378 0.9421 1.0892 1.3509 1.4925 1.7305 1.8641 -0.2392 -0.2745 -0.3959 -0.6159 -0.7909 -0.7265 -0.4868 -0.2912 -0.1679 -0.1267 -0.1267 -0.1396 -0.1633 -0.1784 -0.1771 -0.1566 -0.1178 -0.0674 -0.0207 0.0 0.0 -0.0887 -0.2589 -0.3653 -0.4482 -0.6109 -0.5894 -0.4438 -0.2867 -0.2392 64 | Mo Te Mo Se W S 1.21572465 1.17392465 1.11632465 1.10152465 0.97482465 0.92982465 0.88212465 0.24752465 0.24762465 0.24772465 0.24772465 0.24772465 0.24772465 0.24762465 0.24752465 0.24732465 0.23862465 0.06302465 -0.08797535 -0.15117535 -0.15117535 0.09672465 0.24732465 0.24732465 0.24732465 0.24772465 0.24842465 0.24912465 0.24962465 0.24972465 0.24972465 0.24962465 0.24922465 0.24862465 0.24812465 0.24772465 0.24752465 -0.07887535 0.02472465 0.05452465 0.05452465 -0.01027535 -0.14697535 -0.26077535 -0.31757535 -0.32667535 -0.30277535 -0.26077535 -0.21947535 -0.20107535 -0.20107535 -0.28127535 -0.43007535 -0.47437535 -0.46617535 -0.65507535 -0.78527535 -0.67847535 -0.65107535 -0.65937535 65 | Mo Te Mo Se W Se 1.214 1.214 1.2141 1.2139 1.1485 1.0723 1.1242 1.0142 0.9579 0.9417 0.9417 0.9513 0.9668 0.9585 0.9093 0.8255 0.721 0.6133 0.5263 0.4904 0.4904 0.6347 0.9141 0.9359 0.8126 0.9616 1.2137 1.2141 1.214 1.214 -0.4553 -0.4903 -0.6048 -0.8194 -0.8656 -0.5966 -0.3435 -0.1482 -0.0351 0.0 0.0 -0.044 -0.1334 -0.2038 -0.2348 -0.2312 -0.2013 -0.1561 -0.1129 -0.0938 -0.0938 -0.1768 -0.3339 -0.4054 -0.4418 -0.6356 -0.711 -0.6127 -0.5021 -0.4553 66 | Mo Te Mo Te Mo Te 2.9738 2.789 2.4143 1.8531 1.4507 1.2832 1.3002 1.4149 1.3731 1.3426 1.3426 1.3579 1.3615 1.3488 1.316 1.2615 1.1905 1.1147 1.0529 1.0297 1.0297 1.116 1.2296 1.0932 1.0214 1.2321 1.6485 2.2503 2.7396 2.9738 -0.1087 -0.1257 -0.197 -0.366 -0.6157 -0.8337 -0.7051 -0.5041 -0.3676 -0.3192 -0.3192 -0.2985 -0.2652 -0.2372 -0.2094 -0.1739 -0.127 -0.0721 -0.0222 0.0 0.0 -0.0958 -0.2831 -0.4227 -0.5253 -0.5665 -0.3609 -0.2306 -0.132 -0.1087 67 | Mo Te Mo Te W S 1.23354743 1.19444743 1.14374743 1.19484743 1.12094743 0.98444743 0.88934743 0.31854743 0.31864743 0.02984743 0.02984743 0.31874743 0.31864743 0.31844743 0.31784743 0.30534743 0.14284743 0.31954743 0.31944743 0.31934743 0.31934743 0.31964743 0.31734743 0.31824743 0.31834743 0.31884743 0.31954743 0.32054743 0.32164743 0.32204743 0.32204743 0.32174743 0.32084743 0.31994743 0.31914743 0.31874743 0.31854743 -0.13065257 -0.00905257 -0.02665257 -0.02665257 -0.00565257 -0.07755257 -0.13305257 -0.15455257 -0.14545257 -0.11275257 -0.02955257 -0.02305257 -0.00375257 -0.00375257 0.00554743 -0.25245257 -0.33825257 -0.38125257 -0.54035257 -0.50615257 -0.44795257 -0.51875257 -0.51655257 68 | Mo Te Mo Te W Se 1.1833 1.1833 1.1832 1.1828 1.182 1.1813 1.1811 1.1813 1.1815 1.1816 1.1816 1.1816 1.1815 1.1813 1.1751 1.0492 0.8938 0.7367 0.608 0.5538 0.5538 0.7669 1.1678 1.0637 0.9665 1.1788 1.1825 1.1831 1.1833 1.1833 -0.2676 -0.3031 -0.4115 -0.5262 -0.7494 -0.7351 -0.4908 -0.2899 -0.162 -0.119 -0.119 -0.1338 -0.1608 -0.1776 -0.1762 -0.1552 -0.1164 -0.0665 -0.0204 0.0 0.0 -0.0889 -0.2625 -0.3723 -0.4484 -0.5799 -0.4832 -0.4037 -0.3154 -0.2676 69 | Mo Te Mo Te W Te 2.001 2.001 2.001 1.7256 1.3335 1.1734 1.192 1.3034 1.2367 1.2045 1.2045 1.2174 1.2247 1.2169 1.1853 1.1291 1.0556 0.9773 0.9134 0.8885 0.8885 0.9833 1.1209 0.9985 0.9215 1.1259 1.5298 2.001 2.001 2.001 -0.2534 -0.2695 -0.3368 -0.4934 -0.7336 -0.9665 -0.845 -0.6517 -0.5192 -0.4723 -0.4723 -0.4487 -0.409 -0.3703 -0.3269 -0.2714 -0.1995 -0.1147 -0.0358 0.0 0.0 -0.1371 -0.3559 -0.4809 -0.5673 -0.6545 -0.5123 -0.3661 -0.2755 -0.2534 70 | Mo Te W S W Te 1.21073843 1.17393843 1.12843843 1.18093843 1.09923843 0.96253843 0.86723843 0.30243843 0.30263843 0.30273843 0.30273843 0.30273843 0.30263843 0.30233843 0.30173843 0.28443843 0.11903843 0.30353843 0.30333843 0.10673843 0.10673843 0.30353843 0.30123843 0.30223843 0.30233843 0.30273843 0.30353843 0.30453843 0.30573843 0.30633843 0.30633843 0.30593843 0.30493843 0.30383843 0.30313843 0.30263843 0.30243843 -0.14786157 -0.06396157 -0.02936157 -0.02936157 -0.06456157 -0.13456157 -0.18776157 -0.18696157 -0.15026157 -0.08456157 -0.00196157 0.07363843 -0.05856157 -0.05856157 -0.01486157 -0.22466157 -0.29016157 -0.30666157 -0.49306157 -0.69296157 -0.57666157 -0.48786157 -0.47436157 71 | Mo Te W Se Mo Te 1.214 1.214 1.2138 1.2134 1.2126 1.2118 1.2116 1.2118 1.2121 1.2122 1.2122 1.2122 1.2121 1.2119 1.2061 1.0811 0.9258 0.7688 0.6403 0.5861 0.5861 0.7982 1.1923 1.1713 1.056 1.209 1.2131 1.2137 1.214 1.214 -0.18 -0.2193 -0.3517 -0.5913 -0.8075 -0.7144 -0.474 -0.2785 -0.1558 -0.115 -0.115 -0.1298 -0.1573 -0.1753 -0.1755 -0.1557 -0.1173 -0.0671 -0.0205 0.0 0.0 -0.0885 -0.2582 -0.3629 -0.4434 -0.6105 -0.6145 -0.4062 -0.2329 -0.18 72 | Mo Te W Se W Se 1.1896 1.1897 1.1898 1.1896 1.189 1.1859 1.1884 1.1885 1.1886 1.1886 1.1886 1.1886 1.1887 1.1887 1.1888 1.1887 1.0846 0.9221 0.7895 0.7345 0.7345 0.9446 1.1884 1.0373 0.8984 1.1046 1.1894 1.1897 1.1897 1.1896 -0.4149 -0.4496 -0.5657 -0.7929 -0.8663 -0.5972 -0.3439 -0.1484 -0.0351 0.0 0.0 -0.044 -0.1335 -0.204 -0.2351 -0.2314 -0.2014 -0.1562 -0.1129 -0.0937 -0.0937 -0.1769 -0.3342 -0.4058 -0.4421 -0.6369 -0.7167 -0.5938 -0.4613 -0.4149 73 | Mo Te W Se W Te 1.0384 1.0384 1.0382 1.0377 1.037 1.0362 1.0361 1.0362 1.0365 1.0366 1.0366 1.0366 1.0365 1.0362 1.0266 0.8975 0.7423 0.5856 0.4573 0.4032 0.4032 0.6153 1.0155 1.0239 0.9126 1.0351 1.0374 1.0381 1.0384 1.0384 -0.3378 -0.3768 -0.5086 -0.7241 -0.9026 -0.8328 -0.6082 -0.4419 -0.3396 -0.2985 -0.2985 -0.312 -0.3369 -0.3396 -0.3153 -0.2674 -0.197 -0.1121 -0.0344 0.0 0.0 -0.1327 -0.3434 -0.4359 -0.49 -0.656 -0.7395 -0.5619 -0.3902 -0.3378 74 | Mo Te W Te Mo Te 2.0024 2.0024 2.0023 1.7534 1.3714 1.2221 1.2493 1.3586 1.2506 1.2158 1.2158 1.2246 1.2416 1.2423 1.2105 1.1503 1.0723 0.99 0.9226 0.8947 0.8947 1.0034 1.1713 1.0522 0.9666 1.1691 1.5657 2.0022 2.0024 2.0024 -0.1832 -0.2016 -0.2775 -0.4533 -0.7139 -0.957 -0.8386 -0.6478 -0.5195 -0.4744 -0.4744 -0.447 -0.4016 -0.3602 -0.3171 -0.2634 -0.194 -0.1119 -0.0351 0.0 0.0 -0.1353 -0.354 -0.4816 -0.567 -0.6449 -0.5135 -0.3128 -0.2085 -0.1832 75 | Mo Te W Te W S 1.19143286 1.15243286 1.10143286 1.15223286 1.07983286 0.94263286 0.84653286 0.27703286 0.27713286 0.27733286 0.27733286 0.27723286 0.27713286 0.27693286 0.27633286 0.26103286 0.09683286 0.27803286 0.27793286 0.09813286 0.09813286 0.27813286 0.27583286 0.27673286 0.27693286 0.27733286 0.27803286 0.27913286 0.28013286 0.28063286 0.28063286 0.28023286 0.27943286 0.27843286 0.27763286 0.27723286 0.27703286 -0.14686714 -0.04216714 -0.00726714 -0.00726714 -0.04186714 -0.10976714 -0.15786714 -0.16836714 -0.14196714 -0.08416714 -0.00676714 0.06593286 -0.06086714 -0.06086714 -0.02606714 -0.22846714 -0.29816714 -0.31206714 -0.47686714 -0.53206714 -0.45646714 -0.47206714 -0.48936714 76 | Mo Te W Te W Se 1.0347 1.0347 1.0347 1.0342 1.0335 1.0328 1.0326 1.0327 1.0329 1.0331 1.0331 1.033 1.033 1.0326 1.0229 0.8935 0.738 0.5811 0.4525 0.3982 0.3982 0.6115 1.0187 1.0198 0.9123 1.0323 1.0339 1.0346 1.0347 1.0347 -0.3785 -0.41 -0.4895 -0.6208 -0.8453 -0.8342 -0.6067 -0.4274 -0.3104 -0.2705 -0.2705 -0.2826 -0.3028 -0.3098 -0.2947 -0.2544 -0.1897 -0.1089 -0.0336 0.0 0.0 -0.1303 -0.3402 -0.4362 -0.4885 -0.6317 -0.6059 -0.4992 -0.4203 -0.3785 77 | Mo Te W Te W Te 1.9695 1.9695 1.9695 1.741 1.3687 1.2226 1.2438 1.3256 1.2171 1.182 1.182 1.1909 1.208 1.2092 1.178 1.1178 1.0392 0.9563 0.8885 0.8605 0.8605 0.9709 1.1565 1.0632 0.9745 1.1736 1.5608 1.9694 1.9695 1.9695 -0.2111 -0.2299 -0.3067 -0.4811 -0.738 -0.9831 -0.8699 -0.6867 -0.562 -0.5177 -0.5177 -0.4881 -0.4349 -0.3836 -0.3327 -0.2731 -0.1995 -0.1147 -0.036 0.0 0.0 -0.1408 -0.3679 -0.4989 -0.5829 -0.6636 -0.5422 -0.3425 -0.2368 -0.2111 78 | W S Mo S W S 1.6407 1.6407 1.6408 1.6407 1.6404 1.6402 1.64 1.64 1.6399 1.6399 1.6399 1.64 1.64 1.6401 1.6401 1.6402 1.583 1.4508 1.3449 1.3019 1.3019 1.4689 1.6401 1.6397 1.5596 1.5867 1.6405 1.6408 1.6407 1.6407 0.0 -0.0418 -0.1835 -0.4457 -0.794 -1.1209 -1.2779 -1.206 -1.0473 -0.973 -0.973 -0.9096 -0.8026 -0.696 -0.5998 -0.5004 -0.3888 -0.2685 -0.1634 -0.1177 -0.1177 -0.3 -0.6374 -0.8842 -0.9896 -0.8949 -0.5787 -0.2491 -0.0564 0.0 79 | W S Mo Se W S 1.1852 1.1852 1.1852 1.185 1.1847 1.1844 1.1843 1.1843 1.1843 1.1843 1.1843 1.1843 1.1843 1.1844 1.1844 1.1845 1.1845 1.1845 1.1603 1.0953 1.0953 1.1845 1.1844 1.1841 1.1837 1.1842 1.1849 1.1852 1.1852 1.1852 -0.2711 -0.3072 -0.4275 -0.6262 -0.8446 -1.0268 -0.7732 -0.585 -0.4772 -0.4433 -0.4433 -0.3936 -0.3289 -0.284 -0.2466 -0.2032 -0.1472 -0.0824 -0.0249 0.0 0.0 -0.11 -0.3426 -0.526 -0.6477 -0.7709 -0.6977 -0.4806 -0.3198 -0.2711 80 | W S Mo Se W Se 0.9142 0.9142 0.9141 0.9138 0.9133 0.913 0.9129 0.9129 0.9129 0.9129 0.9129 0.9129 0.9129 0.913 0.913 0.913 0.9129 0.9088 0.7589 0.6938 0.6938 0.9119 0.9129 0.9127 0.9125 0.9129 0.9136 0.914 0.9142 0.9142 -0.3312 -0.3576 -0.4391 -0.6043 -0.8587 -1.1217 -1.1 -0.9279 -0.8164 -0.7794 -0.7794 -0.6832 -0.5733 -0.4846 -0.4071 -0.3253 -0.2308 -0.128 -0.0386 0.0 0.0 -0.1543 -0.438 -0.6329 -0.7431 -0.811 -0.6741 -0.4751 -0.3663 -0.3312 81 | W S Mo Te W S 0.22860724 0.22850724 0.22840724 0.22800724 0.22750724 0.22720724 0.22600724 0.22600724 0.22610724 0.22610724 0.22610724 0.22610724 0.22610724 0.22610724 0.22610724 0.22610724 0.22580724 0.16390724 0.00810724 -0.05569276 -0.05569276 0.19340724 0.22600724 0.22580724 0.22570724 0.22610724 0.22680724 0.22730724 0.22750724 0.22760724 0.22760724 0.22750724 0.22740724 0.22700724 0.22650724 0.22620724 -0.17389276 0.01640724 0.10870724 0.13260724 0.13260724 0.04640724 -0.13829276 -0.29759276 -0.38129276 -0.40279276 -0.38419276 -0.34449276 -0.30449276 -0.05659276 -0.05659276 -0.36479276 -0.50669276 -0.52349276 -0.46339276 -0.63439276 -0.82199276 -0.68119276 -0.61469276 -0.60169276 82 | W S W S W S 1.5646 1.5647 1.5648 1.5647 1.5645 1.5642 1.5641 1.5641 1.564 1.564 1.564 1.564 1.5641 1.5641 1.5642 1.5643 1.5643 1.5643 1.5643 1.5642 1.5642 1.5643 1.5642 1.5638 1.5627 1.5636 1.5646 1.5648 1.5647 1.5646 0.0 -0.0449 -0.1962 -0.4758 -0.846 -1.1835 -1.3585 -1.3218 -1.1781 -1.1103 -1.1103 -1.0099 -0.8719 -0.7581 -0.6587 -0.5581 -0.4467 -0.3277 -0.2238 -0.1784 -0.1784 -0.362 -0.7056 -0.9599 -1.0661 -0.9616 -0.6165 -0.2659 -0.0605 0.0 83 | W S W S W Se 0.9622 0.9622 0.9623 0.9621 0.9617 0.9615 0.9613 0.9613 0.9613 0.9613 0.9613 0.9613 0.9613 0.9614 0.9614 0.9615 0.9615 0.9615 0.9308 0.8662 0.8662 0.9615 0.9614 0.9611 0.9608 0.9612 0.9619 0.9622 0.9623 0.9622 -0.4285 -0.4688 -0.5871 -0.74 -0.9545 -1.1816 -0.9526 -0.7964 -0.7139 -0.6893 -0.6893 -0.6248 -0.5354 -0.4634 -0.3962 -0.3198 -0.2278 -0.1262 -0.0379 0.0 0.0 -0.152 -0.4327 -0.6147 -0.7119 -0.826 -0.7837 -0.6281 -0.4824 -0.4285 84 | W S W Se W Se 0.8924 0.8923 0.8922 0.8919 0.8915 0.8912 0.8911 0.891 0.8911 0.8911 0.8911 0.8911 0.8911 0.8911 0.8912 0.8912 0.8911 0.8849 0.7326 0.6675 0.6675 0.8898 0.8911 0.8909 0.8907 0.8911 0.8917 0.8922 0.8923 0.8924 -0.2685 -0.2966 -0.3905 -0.576 -0.8503 -1.1237 -1.1152 -0.95 -0.8573 -0.8284 -0.8284 -0.7109 -0.5838 -0.4891 -0.4082 -0.3246 -0.2297 -0.1273 -0.0385 0.0 0.0 -0.1555 -0.4441 -0.6443 -0.7543 -0.8116 -0.6546 -0.4326 -0.3063 -0.2685 85 | W S W Te W S 0.22953647 0.22943647 0.22923647 0.22883647 0.22833647 0.22803647 0.22783647 0.22783647 0.22793647 0.22793647 0.22793647 0.22793647 0.22793647 0.22793647 0.22793647 0.22793647 0.22763647 0.15623647 0.00083647 -0.06366353 -0.06366353 0.18633647 0.22783647 0.22763647 0.22753647 0.22793647 0.22863647 0.22913647 0.22943647 0.22943647 0.22943647 0.22943647 -0.56436353 -0.90506353 -0.61426353 -0.31546353 -0.07196353 0.07013647 0.12393647 0.13483647 0.13483647 0.03543647 -0.16636353 -0.32266353 -0.38626353 -0.37846353 -0.32446353 -0.24696353 -0.17516353 -0.06386353 -0.06386353 -0.26576353 -0.45136353 -0.44666353 -0.34056353 -0.49536353 -0.75936353 -0.53216353 -0.37576353 -0.31806353 86 | W S W Te W Te 0.29090036 0.29040036 0.28940036 0.28840036 0.28780036 0.28740036 0.28720036 0.28720036 0.28740036 0.28740036 0.28740036 0.28740036 0.28740036 0.28720036 0.28680036 0.28390036 0.14260036 -0.04949964 -0.20549964 -0.27119964 -0.27119964 -0.01199964 0.28630036 0.28690036 0.28700036 0.28750036 0.28810036 -0.06099964 0.13460036 0.17060036 0.17060036 0.14500036 0.00090036 -0.37849964 -0.89759964 -1.32119964 -1.40689964 -1.07489964 -0.84509964 -0.76289964 -0.76289964 -0.77709964 -0.80399964 -0.82129964 -0.81759964 -0.78649964 -0.72409964 -0.63379964 -0.53709964 -0.48849964 -0.48849964 -0.67809964 -0.93199964 -1.02229964 -1.10139964 -0.84319964 -0.45799964 -0.78539964 -0.53499964 -0.45259964 87 | W Se Mo S W Se 1.6137 1.5582 1.4642 1.4016 1.2796 1.2054 1.087 1.022 0.9982 0.994 0.994 1.0019 1.0092 0.9831 0.9089 0.7955 0.6596 0.5217 0.4109 0.3653 0.3653 0.5488 0.9188 1.1837 1.026 1.068 1.2785 1.4104 1.5401 1.6137 -0.3195 -0.3534 -0.4583 -0.6242 -0.8603 -1.1209 -1.1103 -0.9467 -0.8552 -0.8266 -0.8266 -0.7067 -0.5812 -0.489 -0.4101 -0.3273 -0.2321 -0.1287 -0.0388 0.0 0.0 -0.155 -0.439 -0.6342 -0.7451 -0.8121 -0.6865 -0.5014 -0.3649 -0.3195 88 | W Se Mo Se W Se 1.7859 1.7859 1.786 1.7851 1.5657 1.5446 1.6589 1.6654 1.6373 1.633 1.633 1.6408 1.6534 1.6464 1.6036 1.5277 1.4305 1.3293 1.2478 1.2145 1.2145 1.3436 1.5528 1.4723 1.3096 1.3851 1.642 1.786 1.7859 1.7859 -0.1322 -0.1632 -0.2718 -0.4809 -0.7641 -1.0325 -1.1628 -1.0552 -0.9358 -0.8958 -0.8958 -0.7292 -0.5995 -0.4994 -0.4147 -0.329 -0.233 -0.1297 -0.0393 0.0 0.0 -0.1564 -0.4417 -0.6456 -0.7597 -0.7755 -0.5789 -0.3226 -0.1741 -0.1322 89 | W Se Mo Se W Te 1.1441 1.1441 1.1442 1.1441 1.0933 1.0461 1.0534 0.9421 0.8848 0.8682 0.8682 0.8778 0.8934 0.885 0.8357 0.7515 0.6467 0.5388 0.4515 0.4156 0.4156 0.5605 0.8463 0.9444 0.8136 0.9208 1.1438 1.1442 1.1442 1.1441 -0.5182 -0.5255 -0.6313 -0.8477 -0.8423 -0.5755 -0.343 -0.1843 -0.1047 -0.0826 -0.0826 -0.1306 -0.2204 -0.2775 -0.2857 -0.253 -0.1891 -0.1075 -0.0327 0.0 0.0 -0.1269 -0.3269 -0.382 -0.3822 -0.5671 -0.7617 -0.6136 -0.5282 -0.5182 90 | W Se Mo Te W Se 1.191 1.1911 1.1911 1.191 1.1904 1.1612 1.1896 1.1898 1.1899 1.19 1.19 1.19 1.19 1.1901 1.1902 1.1901 1.0916 0.9284 0.7961 0.7411 0.7411 0.9526 1.1897 1.0003 0.8639 1.0811 1.1908 1.1911 1.1911 1.191 -0.513 -0.5417 -0.5955 -0.806 -0.8655 -0.596 -0.3425 -0.1474 -0.0348 0.0 0.0 -0.0453 -0.1376 -0.2103 -0.2422 -0.2387 -0.2087 -0.1634 -0.1202 -0.1011 -0.1011 -0.1841 -0.3409 -0.4111 -0.4453 -0.6376 -0.7062 -0.5893 -0.5455 -0.513 91 | W Se W S W Se 0.9176 0.9176 0.9174 0.9171 0.9166 0.9163 0.9162 0.9162 0.9162 0.9162 0.9162 0.9162 0.9163 0.9163 0.9163 0.9163 0.9162 0.9077 0.7539 0.6888 0.6888 0.9147 0.9162 0.916 0.9158 0.9162 0.9169 0.9173 0.9176 0.9176 -0.2434 -0.2881 -0.4255 -0.626 -0.8632 -1.1232 -1.106 -0.9427 -0.8514 -0.823 -0.823 -0.705 -0.5803 -0.4885 -0.4099 -0.3271 -0.2319 -0.1286 -0.0388 0.0 0.0 -0.1548 -0.4388 -0.6335 -0.7439 -0.8122 -0.6898 -0.4802 -0.3033 -0.2434 92 | W Se W Se W Se 1.7712 1.7712 1.7713 1.7714 1.7525 1.7681 1.7705 1.7705 1.7705 1.7704 1.7704 1.7705 1.7706 1.7707 1.7708 1.7708 1.7707 1.6959 1.5678 1.5149 1.5149 1.7027 1.7708 1.7265 1.525 1.5772 1.7713 1.7714 1.7712 1.7712 -0.136 -0.1641 -0.2651 -0.4687 -0.758 -1.0365 -1.189 -1.0898 -0.9896 -0.9573 -0.9573 -0.7597 -0.6115 -0.5046 -0.4163 -0.3288 -0.2322 -0.1292 -0.0393 0.0 0.0 -0.1579 -0.4479 -0.657 -0.771 -0.7785 -0.5649 -0.3131 -0.174 -0.136 93 | W Se W Se W Te 1.1209 1.121 1.1211 1.121 1.1204 1.1197 1.1197 1.1198 1.1199 1.1199 1.1199 1.1199 1.12 1.1201 1.1201 1.12 1.0119 0.8492 0.7168 0.6618 0.6618 0.8725 1.1197 1.1167 0.9814 1.0939 1.1207 1.1211 1.121 1.1209 -0.4688 -0.4893 -0.6011 -0.8309 -0.8417 -0.5744 -0.3414 -0.1823 -0.1025 -0.0802 -0.0802 -0.1287 -0.2192 -0.2771 -0.2856 -0.253 -0.1891 -0.1075 -0.0327 0.0 0.0 -0.1269 -0.327 -0.3818 -0.3813 -0.5667 -0.7652 -0.6052 -0.4961 -0.4688 94 | W Se W Te W Se 1.1288 1.1288 1.1289 1.1287 1.1282 1.1275 1.1275 1.1276 1.1276 1.1277 1.1277 1.1277 1.1278 1.1278 1.1279 1.1278 1.0191 0.8559 0.7235 0.6685 0.6685 0.8807 1.1275 1.118 0.965 1.1178 1.1285 1.1289 1.1288 1.1288 -0.524 -0.5161 -0.6068 -0.8224 -0.84 -0.5726 -0.3398 -0.1808 -0.1012 -0.079 -0.079 -0.128 -0.2195 -0.2778 -0.2861 -0.2532 -0.1891 -0.1074 -0.0326 0.0 0.0 -0.1269 -0.3268 -0.3813 -0.3802 -0.563 -0.7571 -0.5896 -0.5141 -0.524 95 | W Se W Te W Te 0.9981 0.9981 0.998 0.9975 0.9968 0.9961 0.9959 0.996 0.9962 0.9964 0.9964 0.9964 0.9963 0.9959 0.9838 0.8533 0.6981 0.5415 0.4131 0.359 0.359 0.5718 0.98 0.9951 0.9827 0.9957 0.9972 0.9979 0.9981 0.9981 -0.4073 -0.4359 -0.503 -0.6363 -0.8597 -0.8648 -0.6359 -0.4642 -0.3657 -0.335 -0.335 -0.3416 -0.3484 -0.3407 -0.3132 -0.2642 -0.1943 -0.1108 -0.0342 0.0 0.0 -0.1349 -0.354 -0.4542 -0.5029 -0.6402 -0.6329 -0.5137 -0.4447 -0.4073 96 | W Te Mo S W Te 0.97457145 0.92937145 0.86007145 0.88247145 0.81587145 0.62407145 0.46027145 0.33747145 0.26237145 0.23717145 0.23717145 0.23177145 0.20557145 0.14677145 0.05667145 -0.05772855 -0.09602855 -0.01302855 0.06067145 0.09377145 0.09377145 -0.03272855 0.08257145 0.40197145 0.50267145 0.63077145 0.80387145 0.81857145 0.91457145 0.97457145 -0.48872855 -0.49272855 -0.60982855 -0.74472855 -0.83822855 -0.57862855 -0.34872855 -0.18642855 -0.10102855 -0.07612855 -0.07612855 -0.11042855 -0.17122855 -0.20502855 -0.20072855 -0.16242855 -0.09732855 -0.01482855 -0.41722855 -0.46002855 -0.46002855 -0.03462855 -0.23732855 -0.30672855 -0.32892855 -0.51082855 -0.68462855 -0.58532855 -0.49712855 -0.48872855 97 | W Te Mo Se W Te 1.6866 1.5844 1.3938 1.3037 1.1425 1.0645 0.9144 0.7798 0.7 0.6739 0.6739 0.6815 0.6896 0.6698 0.6115 0.5228 0.4163 0.3079 0.2199 0.1833 0.1833 0.3326 0.6409 0.9032 0.8234 0.9531 1.1809 1.3153 1.5533 1.6866 -0.3776 -0.4113 -0.5306 -0.7093 -0.8836 -0.8488 -0.6247 -0.458 -0.3634 -0.3341 -0.3341 -0.3416 -0.3501 -0.3441 -0.3181 -0.2695 -0.1984 -0.1129 -0.0347 0.0 0.0 -0.1337 -0.3456 -0.4401 -0.4965 -0.6551 -0.71 -0.5771 -0.4232 -0.3776 98 | W Te Mo Te W Te 1.9922 1.9923 1.9922 1.7635 1.3821 1.2291 1.2494 1.3422 1.2351 1.2004 1.2004 1.2089 1.2253 1.2263 1.1953 1.1354 1.0573 0.9748 0.9073 0.8795 0.8795 0.9878 1.1566 1.0447 0.9648 1.1729 1.5733 1.9922 1.9923 1.9922 -0.2119 -0.2313 -0.3099 -0.4818 -0.7298 -0.965 -0.8533 -0.6638 -0.5365 -0.4918 -0.4918 -0.4626 -0.4135 -0.3688 -0.3236 -0.2684 -0.1976 -0.114 -0.0357 0.0 0.0 -0.1376 -0.3589 -0.4875 -0.5756 -0.651 -0.4954 -0.3469 -0.2384 -0.2119 99 | W Te W S W Te 1.15067253 1.11437253 1.06947253 1.12207253 1.03937253 0.90257253 0.80677253 0.24497253 0.24517253 0.24527253 0.24527253 0.24527253 0.24517253 0.24487253 0.24427253 0.22357253 0.05717253 0.24607253 0.01487253 0.04567253 0.04567253 -0.07332747 0.24367253 0.24477253 0.24487253 0.24527253 0.24607253 0.24707253 0.24837253 0.24887253 0.24887253 0.24847253 0.24747253 0.24637253 0.24567253 0.24517253 0.24497253 -0.21572747 -0.13142747 -0.10712747 -0.10712747 -0.14412747 -0.21072747 -0.24902747 -0.24702747 -0.20972747 -0.14372747 -0.06092747 0.01247253 -0.31182747 -0.31182747 -0.08222747 -0.28432747 -0.35112747 -0.36942747 -0.55342747 -0.74382747 -0.63652747 -0.53442747 -0.50992747 100 | W Te W Te W Te 1.9593 1.9593 1.9593 1.833 1.4811 1.3683 1.4376 1.6055 1.6602 1.6534 1.6534 1.6601 1.6457 1.6067 1.5358 1.4328 1.3052 1.1686 1.0521 1.0028 1.0028 1.1773 1.3759 1.1779 1.0732 1.277 1.6577 1.9593 1.9593 1.9593 -0.2134 -0.233 -0.3119 -0.4869 -0.7432 -0.9893 -0.8847 -0.7082 -0.5982 -0.5616 -0.5616 -0.5164 -0.4477 -0.3903 -0.3365 -0.2753 -0.2008 -0.1155 -0.0363 0.0 0.0 -0.1429 -0.3736 -0.507 -0.591 -0.6711 -0.5511 -0.3487 -0.2401 -0.2134 101 | -------------------------------------------------------------------------------- /Bandstructure/Strucuture1.png: 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-------------------------------------------------------------------------------- 1 | import sys 2 | import numpy as np 3 | from matplotlib import pyplot as plt 4 | from sklearn.gaussian_process import GaussianProcessRegressor 5 | from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C ,WhiteKernel as Wht,Matern as matk 6 | from sklearn.gaussian_process.kernels import RationalQuadratic as expker 7 | from sklearn.metrics import mean_squared_error as MSError 8 | from scipy.stats import norm 9 | 10 | inputmap=dict() 11 | ninputmap=dict() 12 | totfea_atom=2 #total number of atoms per layer 13 | n_3layer_atoms=6 # number of atoms in 3 layer 14 | natom_layer=n_3layer_atoms*totfea_atom #total number of features 15 | Niteration = 30 # number of iteration in a given Bayesian Optimization 16 | #input parameters 17 | train_test_split=0.10 # initial sampled data in a given Bayesian Optimization run 18 | Nruns = 1 # total number of Bayesian Optimization runs 19 | 20 | 21 | #create input feature vector of the given n-layer heterostructure 22 | def createinputmap(inputmap,ninputmap,totfea_atom): 23 | #define the eletronegetivity and ionization potential of each atoms 24 | inputmap['Mo'] = [2.16,684.3,190.0] 25 | inputmap['W'] = [2.36,770.0,193.0] 26 | inputmap['S'] = [2.58,999.6,88.8] 27 | inputmap['Se'] = [2.55,941.0,103.0] 28 | inputmap['Te'] = [2.10,869.3,123.0] 29 | 30 | #normalize the input features by (tt-xmax)/(xmax-xmin) 31 | Xmax = np.empty(totfea_atom,dtype=float) 32 | Xmin = np.empty(totfea_atom, dtype=float) 33 | Xmean= np.empty(totfea_atom,dtype=float) 34 | Xstd = np.empty(totfea_atom,dtype=float) 35 | Xmax.fill(0.0) 36 | Xmin.fill(10000.0) 37 | Xmean.fill(0.0) 38 | Xstd.fill(0.0) 39 | nfeatures=0 40 | for keys in inputmap: 41 | nfeatures+=1 42 | for ii in range(0,totfea_atom): 43 | if Xmax[ii] < inputmap[keys][ii]: Xmax[ii]=inputmap[keys][ii] 44 | if Xmin[ii] > inputmap[keys][ii]: Xmin[ii]=inputmap[keys][ii] 45 | Xmean[ii]+=inputmap[keys][ii] 46 | for ii in range(0,totfea_atom): 47 | Xmean[ii]=Xmean[ii]/float(nfeatures) 48 | for keys in inputmap: 49 | for ii in range(0, totfea_atom): 50 | Xstd[ii]+=(inputmap[keys][ii]- Xmean[ii])*(inputmap[keys][ii]- Xmean[ii]) 51 | for ii in range(0, totfea_atom): 52 | Xstd[ii]=np.sqrt(Xstd[ii]/float(nfeatures)) 53 | print("Xmax and Xmin: ",Xmax,Xmin) 54 | print("Xmean and Xstd: ",Xmean,Xstd) 55 | for keys in inputmap: 56 | ninputmap[keys]=list() 57 | for ii in range(0, totfea_atom): 58 | ninputmap[keys].append((inputmap[keys][ii]-Xmin[ii])/(Xmax[ii]-Xmin[ii])) # normalized by by (tt-xmax)/(xmax-xmin) 59 | # ninputmap[keys].append((inputmap[keys][ii]-Xmean[ii])/Xstd[ii]) 60 | #print the final keys: 61 | for keys in inputmap: 62 | print("key :", keys,inputmap[keys]) 63 | for keys in ninputmap: 64 | print("nkey :", keys, ninputmap[keys]) 65 | 66 | #read input data 67 | def readinput(filename,natom_layer): 68 | inputfile=open(filename,'r') 69 | dataset=list() 70 | itag=0 71 | count=-1 72 | ndata=0 73 | for lines in inputfile: 74 | if itag==0: 75 | ndata=int(lines) 76 | Xdata = np.ndarray(shape=(ndata, natom_layer), dtype=float) 77 | Xinfo = np.chararray(ndata, itemsize=20) 78 | Ydata = np.empty(ndata,dtype=float) 79 | itag=1 80 | else : 81 | lines = lines.replace("\n", "").split() 82 | structname=str() 83 | count+=1 84 | for ii in range(0,lines.__len__()-1): 85 | jj=lines[ii] 86 | if (ii > 0) : structname = structname + '-' + jj 87 | else: structname=jj 88 | # print(3*ii,3*ii+1,3*ii+2,jj,inputmap[jj][0],inputmap[jj][1],inputmap[jj][2]) 89 | Xdata[count][2 * ii] = ninputmap[jj][0] 90 | Xdata[count][2 * ii + 1] = ninputmap[jj][1] 91 | Xinfo[count]=structname 92 | Ydata[count] = float(lines[lines.__len__() - 1]) 93 | print("structname: ",structname,lines,lines.__len__()) 94 | #print the entire dataset 95 | # for ii in range(0,ndata): 96 | # print("data: ",ii,Xdata[ii][:],Ydata[ii]) 97 | return Xdata,Ydata,Xinfo,ndata 98 | 99 | #building a gaussian process regression model 100 | def gpregression(Xtrain,Ytrain,Nfeature): 101 | cmean=[1.0]*Nfeature 102 | cbound=[[1e-3, 1000]]*Nfeature 103 | kernel = C(1.0, (1e-3, 1e3)) * matk(cmean, cbound, 1.5) 104 | 105 | gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=40, normalize_y=False) 106 | gp.fit(Xtrain, Ytrain) 107 | return gp 108 | 109 | #predict result using GP regression model 110 | def gprediction(gpnetwork,xtest): 111 | y_pred, sigma = gpnetwork.predict(xtest, return_std=True) 112 | return y_pred, sigma 113 | 114 | #compute expected improvement 115 | def expectedimprovement(xdata,gpnetwork,ybest,itag,epsilon): 116 | ye_pred, esigma = gprediction(gpnetwork, xdata) 117 | expI = np.empty(ye_pred.size, dtype=float) 118 | for ii in range(0,ye_pred.size): 119 | if esigma[ii] > 0: 120 | zzval=itag*(ye_pred[ii]-ybest)/float(esigma[ii]) 121 | expI[ii]=itag*(ye_pred[ii]-ybest-epsilon)*norm.cdf(zzval)+esigma[ii]*norm.pdf(zzval) 122 | else: 123 | expI[ii]=0.0 124 | return expI 125 | 126 | #Bayesian optimization run 127 | def numberofopt(Xdata,Ydata,Xinfo,ndata,natom_layer,totfea_atom): 128 | itag = 1 129 | epsilon = 0.1 130 | ntrain = int(train_test_split * ndata) 131 | nremain = ndata - ntrain 132 | dataset = np.random.permutation(ndata) 133 | a1data = np.empty(ntrain, dtype=int) 134 | a2data = np.empty(nremain, dtype=int) 135 | a1data[:] = dataset[0:ntrain] 136 | a2data[:] = dataset[ntrain:ndata] 137 | # info for the initial training set 138 | Xtrain = np.ndarray(shape=(ntrain, natom_layer), dtype=float) 139 | Xtraininfo = np.chararray(ntrain, itemsize=20) 140 | Ytrain = np.empty(ntrain, dtype=float) 141 | Xtrain[0:ntrain, :] = Xdata[a1data, :] 142 | Xtraininfo[0:ntrain] = Xinfo[a1data] 143 | Ytrain[0:ntrain] = Ydata[a1data] 144 | yopttval = np.max(Ytrain) 145 | xoptval = Xtraininfo[np.argmax(Ytrain)] 146 | yoptstep=0 147 | yopinit = yopttval 148 | xoptint = xoptval 149 | # info for the remaining data set 150 | Xremain = np.ndarray(shape=(nremain, natom_layer), dtype=float) 151 | Xremaininfo = np.chararray(nremain, itemsize=20) 152 | Yremain = np.empty(nremain, dtype=float) 153 | Xremain[0:nremain, :] = Xdata[a2data, :] 154 | Xremaininfo[0:nremain] = Xinfo[a2data] 155 | Yremain[0:nremain] = Ydata[a2data] 156 | print("Xremain: ", Xremain.shape) 157 | print("Yremain:", Yremain.shape) 158 | print("Xremaininfo: ", Xremaininfo.shape) 159 | print("Initial max value 0th run : ", xoptval, yopttval) 160 | print("Total number of inital training points: ", ntrain) 161 | # print("Xtrain: ",Xtrain) 162 | for ii in range(0, Niteration): 163 | if ii > int(0.5*Niteration): 164 | epsilon=0.01 165 | print("updated epsilon: ",epsilon) 166 | gpnetwork = gpregression(Xtrain, Ytrain, natom_layer) 167 | yt_pred, tsigma = gprediction(gpnetwork, Xtrain) 168 | ybest = np.max(yt_pred) 169 | ybestloc = np.argmax(yt_pred) 170 | print("current Best in iteration ii", ii + 1, " is ", ybest, "for the structure: ", Xtraininfo[ybestloc]) 171 | if yopttval < ybest: 172 | yopttval = ybest 173 | xoptval = Xtraininfo[ybestloc] 174 | print("Best Strucutre so far", yopttval, "for the structure: ", xoptval) 175 | expI = expectedimprovement(Xremain, gpnetwork, ybest, itag, epsilon) 176 | expImax = np.max(expI) 177 | expimaxloc = np.argmax(expI) 178 | print("Next Structure to evaluate has expI ", expImax, "for the structure: ", Xremaininfo[expimaxloc], 179 | "has Y: ", Yremain[expimaxloc]) 180 | xnew = np.append(Xtrain, Xremain[expimaxloc]).reshape(-1, natom_layer) 181 | xnewinfo = np.append(Xtraininfo, Xremaininfo[expimaxloc]) 182 | ynew = np.append(Ytrain, Yremain[expimaxloc]) 183 | xrnew = np.delete(Xremain, expimaxloc, 0) 184 | xrnewinfo = np.delete(Xremaininfo, expimaxloc) 185 | yrnew = np.delete(Yremain, expimaxloc) 186 | if ii==0: 187 | Xexplored=Xremaininfo[expimaxloc] 188 | Yexplored=Yremain[expimaxloc] 189 | else: 190 | Xexploredtemp=np.append(Xexplored, Xremaininfo[expimaxloc]) 191 | Yexploredtemp=np.append(Yexplored, Yremain[expimaxloc]) 192 | del Xexplored,Yexplored 193 | Xexplored=Xexploredtemp 194 | Yexplored=Yexploredtemp 195 | # print("Xremain info: ",xrnew.shape,yrnew.shape,xrnewinfo.shape) 196 | del Xtrain, Ytrain, Xremaininfo, gpnetwork 197 | Xtrain = xnew 198 | Xtraininfo = xnewinfo 199 | Ytrain = ynew 200 | Xremain = xrnew 201 | Xremaininfo = xrnewinfo 202 | Yremain = yrnew 203 | del xnew, xnewinfo, ynew, xrnew, xrnewinfo, yrnew 204 | 205 | if not yopinit==yopttval: 206 | yoptstep=np.argmax(Yexplored)+1 207 | else: 208 | yoptstep=0 209 | dataorder = np.argsort(Yexplored) 210 | Yexploredtemp=Yexplored[dataorder] 211 | Xexploredtemp = Xexplored[dataorder] 212 | print(Yexplored) 213 | Xbest=Xexploredtemp[Niteration-3:Niteration] 214 | Ybest=Yexploredtemp[Niteration - 3:Niteration] 215 | print("\n") 216 | print("Initial Best Strucuture: ", xoptint, "has value: ", yopinit) 217 | print("Final Optimal Strucuture: ", xoptval, "has value: ", yopttval,"in step: ",yoptstep) 218 | print("Final Best Structure 1st: ",Xbest[2],"has value: ", Ybest[2]) 219 | print("Final Best Structure 2st: ", Xbest[1],"has value: ", Ybest[1]) 220 | print("Final Best Structure 2st: ", Xbest[0],"has value: ", Ybest[0]) 221 | return xoptint,yopinit,xoptval,yopttval 222 | 223 | 224 | #------- Program Starts from here ------------- 225 | createinputmap(inputmap,ninputmap,totfea_atom) 226 | in_file=sys.argv[1] 227 | Xdata,Ydata,Xinfo,ndata=readinput(in_file,natom_layer) 228 | print("Original Training X and Y :",np.shape(Xdata),np.shape(Xdata)) 229 | 230 | Xinitguess = np.chararray(Nruns, itemsize=20) 231 | Yinitguess = np.empty(Nruns, dtype=float) 232 | Xoptimal = np.chararray(Nruns, itemsize=20) 233 | Yoptimal = np.empty(Nruns, dtype=float) 234 | 235 | for ii in range(0,Nruns): 236 | Xinitguess[ii], Yinitguess[ii], Xoptimal[ii], Yoptimal[ii] =numberofopt(Xdata, Ydata, Xinfo, ndata, natom_layer, totfea_atom) 237 | 238 | print("\n-----Final Result------\n") 239 | for ii in range(0,Nruns): 240 | print("Initial Best Strucuture: ", Xinitguess[ii], " has value: ", Yinitguess[ii]," Final Optimal Strucuture: ", Xoptimal[ii], " has value: ", Yoptimal[ii]) 241 | -------------------------------------------------------------------------------- /Highlight_Figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rajak7/Bayesian_Optimization_Material_design/146233e4c5efa4cfcbf915f32cb8926147f98b2d/Highlight_Figure.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /N_doped_EFF_max.txt: -------------------------------------------------------------------------------- 1 | W S W S W S 4.09561322387 2 | W Se W Se W Se 3.49571991673 3 | W Te W Te W Te 18.3464572843 4 | W Se Mo Te W Se 17.4152205082 5 | W Se Mo Se W Se 9.95439008829 6 | W S W S W Se 26710.04729 7 | W S W Se W Te 3.83864338846 8 | W Se Mo Se W Te 11.3368388874 9 | W S W Se W S 17.5737884116 10 | W Te Mo Se W Te 8.1469178951 11 | W S W S W Te 4.89555065683 12 | W S W Te W Te 3.61381125152 13 | W Se W S W Se 14.5767934642 14 | W Se Mo S W Se 11.1413429064 15 | W Se Mo Te W Te 15.3084855619 16 | W S W Se W Se 17.8819083261 17 | Mo Te Mo Se W Te 11.1488276974 18 | W S W Te W Se 2.38673190187 19 | Mo Te W S W S 4.82654367093 20 | Mo Te W Te Mo Te 10.7927946411 21 | W Se Mo S W Te 1.24393997841 22 | Mo Te W S Mo Te 1.46809299801 23 | W S Mo Se W Se 16.8404337904 24 | Mo Te Mo Te W S 2.01981487991 25 | W S Mo Se W S 17.3994847622 26 | Mo Te Mo Te W Se 15.8706087721 27 | Mo Te Mo Se W Se 11.7740758821 28 | Mo Te W Se Mo Te 12.8387134309 29 | Mo Te W Te W Te 12.6208456623 30 | Mo Te W Se W Te 16.3482079291 31 | Mo Te Mo Se Mo Te 10.3759971846 32 | W S Mo S W S 9.76404421185 33 | W S W Te W S 3.71647048618 34 | Mo Te W S W Se 2.66196193956 35 | Mo Te W Se W Se 20.3089988911 36 | Mo Se W Te W Se 11.5441782833 37 | Mo Te W S W Te 2.50932937955 38 | W S Mo S W Se 12.8173848343 39 | Mo Se Mo Te Mo Se 12.0613378438 40 | Mo Se W Te W Te 9.90996614914 41 | Mo Te Mo S W Se 1.02775249888 42 | Mo Te W Te W Se 15.6303729872 43 | Mo Se Mo Te Mo Te 12.3736286281 44 | Mo Se W S W Se 17.9228102182 45 | Mo Te W Se W S 3.02222603106 46 | Mo Se W S Mo Se 14.30618128 47 | Mo Se Mo Te W Te 12.2371562268 48 | Mo Se Mo Te W Se 11.5436541074 49 | W S Mo Te W Se 2.58558501877 50 | Mo Te Mo S Mo Te 0.574209398469 51 | Mo Te Mo Se W S 3.72654875064 52 | Mo Se W Te Mo Se 12.0728615362 53 | Mo Se Mo Te W S 2.57823750168 54 | Mo Se W Se Mo Te 11.4380564512 55 | Mo Se W Te Mo Te 10.5289788465 56 | Mo Se W Se W Se 11.5354699766 57 | W S Mo S W Te 1.67070075494 58 | Mo Se W S Mo Te 2.86550791496 59 | Mo Se W Se Mo Se 11.3372928344 60 | Mo Se W Se W S 17.4712770774 61 | Mo Se Mo S Mo Se 10.9727569793 62 | Mo Se W S W S 21.2177304137 63 | Mo Se W Se W Te 946.467219472 64 | Mo S W Se W Te 1.88340663598 65 | Mo S W Te Mo S 0.895757291453 66 | Mo Se Mo S W Se 13.340713038 67 | Mo S W Te W S 2.04915311075 68 | Mo Se Mo S Mo Te 1.06509034723 69 | Mo Se W Te W S 2.45477113295 70 | Mo S W Se Mo Se 13.2491361948 71 | Mo S W Se W Se 13.1809884748 72 | Mo S Mo S Mo S 5.70169932932 73 | Mo Se Mo Se Mo Te 14.0879612246 74 | Mo Se Mo Se W S 16.4224040902 75 | Mo Se Mo S W Te 1.12799153732 76 | Mo Se Mo Se W Te 13.6356435303 77 | Mo S W Te Mo Te 2.84122300532 78 | Mo Se W S W Te 2.57902195291 79 | Mo S W Se Mo S 13.5517394094 80 | Mo S W S W Te 3.85227042959 81 | Mo Se Mo Se W Se 16.5625937753 82 | Mo S Mo Se Mo Se 13.0066215751 83 | Mo S Mo Te W Te 2.60047654696 84 | Mo S W S Mo S 12.2280570077 85 | Mo S W Se W S 12.96289253 86 | Mo S W S W Se 12.9231549584 87 | W S Mo Te W Te 2.3892817052 88 | Mo S Mo S W S 16.0009856669 89 | Mo S W Te W Te 4.72061679744 90 | Mo S Mo Se Mo S 13.531777181 91 | Mo Te Mo S W S 1.64643779702 92 | Mo S Mo Te Mo Se 1.1950865518 93 | Mo S Mo Te W S 2.47251132738 94 | Mo S W Se Mo Te 1.70127513891 95 | Mo S Mo S W Se 16.4507133063 96 | Mo S Mo Te Mo Te 1.1973077205 97 | Mo S W S Mo Se 12.8421725402 98 | Mo S Mo Se W Se 13.7557807249 99 | Mo S Mo Se W S 13.0208606641 100 | Mo S W Te Mo Se 1.13312167355 101 | Mo S Mo Te Mo S 0.887002502813 102 | Mo S Mo S Mo Se 16.2253967378 103 | Mo S Mo Se Mo Te 2.09989804283 104 | Mo S Mo S W Te 2.24214604223 105 | Mo S Mo S Mo Te 2.22794788605 106 | Mo Se Mo S W S 12.731492264 107 | Mo S Mo Se W Te 2.16751396262 108 | Mo S W S W S 12.3191489485 109 | W Se W S W Te 2.5580871884 110 | W Se W Se W Te 19.7825593726 111 | W Se W Te W Se 20.0255248599 112 | W Se W Te W Te 16.1016127323 113 | W Te Mo Te W Te 14.305081592 114 | W Te Mo S W Te 0.642227377998 115 | W Te W Se W Te 13.2954737073 116 | W Te W S W Te 1.44218942484 117 | -------------------------------------------------------------------------------- /P_doped_EFF_max.txt: -------------------------------------------------------------------------------- 1 | 115 2 | W S W S W S 4.95564804923 3 | W Se W Se W Se 5.12864556538 4 | W Te W Te W Te 12.3313584777 5 | W Se Mo Te W Se 9.14200623134 6 | W Se Mo Se W Se 13.6847155111 7 | W S W Se W Te 5.76556112746 8 | W Se Mo Se W Te 11.9344980347 9 | W S W Se W S 10.4527790356 10 | W Te Mo Se W Te 10.4738226581 11 | W S W S W Te 5.19070846257 12 | W S W Te W Te 2.67992512669 13 | W Se W S W Se 13.4288606349 14 | W Se Mo S W Se 13.2462517722 15 | W Se Mo Te W Te 10.9162720605 16 | W S W Se W Se 13.0056967745 17 | Mo Te Mo Se W Te 10.1336431523 18 | W S W Te W Se 4.01842632646 19 | Mo Te W S W S 4.44748774095 20 | Mo Te W Te Mo Te 10.2410669509 21 | W Se Mo S W Te 2.42647722982 22 | Mo Te W S Mo Te 2.7419301144 23 | W S Mo Se W Se 11.5849921365 24 | Mo Te Mo Te W S 2.79085061483 25 | W S Mo Se W S 8.05617571993 26 | Mo Te Mo Te W Se 8.16791543225 27 | Mo Te Mo Se W Se 11.3622805847 28 | Mo Te W Se Mo Te 8.92611691825 29 | Mo Te W Te W Te 12.8972923655 30 | Mo Te W Se W Te 11.0485456726 31 | Mo Te Mo Se Mo Te 8.86094822085 32 | W S Mo S W S 5.96694258632 33 | W S W Te W S 2.18969285389 34 | Mo Te W Te W S 5.01027927749 35 | Mo Te W S W Se 3.07977293833 36 | Mo Te W Se W Se 11.6657777129 37 | Mo Se W Te W Se 11.5360825163 38 | Mo Te W S W Te 3.85434066673 39 | W S Mo S W Se 11.36368101 40 | Mo Se Mo Te Mo Se 8.87124116077 41 | Mo Se W Te W Te 9.43411216381 42 | Mo Te Mo S W Se 1.39326932281 43 | Mo Te W Te W Se 10.9851834212 44 | Mo Se Mo Te Mo Te 7.94289899181 45 | Mo Se W S W Se 12.5722553622 46 | Mo Te Mo S W Te 3.17650687134 47 | Mo Te W Se W S 5.04502013597 48 | Mo Se W S Mo Se 11.1141482738 49 | Mo Se Mo Te W Te 10.2502717667 50 | Mo Se Mo Te W Se 11.6463008798 51 | W S Mo Te W Se 3.39605805441 52 | Mo Te Mo S Mo Te 1.65734875596 53 | Mo Te Mo Se W S 4.7750899854 54 | Mo Se W Te Mo Se 9.60739727576 55 | Mo Se Mo Te W S 3.03384250208 56 | Mo Se W Se Mo Te 11.6184813884 57 | Mo Se W Te Mo Te 9.14822715597 58 | Mo Se W Se W Se 13.6952331226 59 | W S Mo S W Te 3.08994868069 60 | Mo Se W S Mo Te 3.41008138717 61 | Mo Se W Se Mo Se 10.8803589647 62 | Mo Se W Se W S 11.7535920004 63 | Mo Se Mo S Mo Se 10.6270936992 64 | Mo Se W S W S 8.67823365484 65 | Mo S W Se W Te 4.34918163648 66 | Mo Se Mo S W Se 11.7045972926 67 | Mo S W Te W S 2.14065827778 68 | Mo Se Mo S Mo Te 1.50827796399 69 | Mo Se W Te W S 3.87154040365 70 | Mo S W Se Mo Se 12.4229866527 71 | Mo S W Se W Se 13.0485869761 72 | Mo S Mo S Mo S 4.634630125 73 | Mo Se Mo Se Mo Te 11.5720718389 74 | Mo Se Mo Se W S 10.229769044 75 | Mo Se Mo S W Te 2.21085252835 76 | Mo Se Mo Se W Te 11.4140048685 77 | Mo S W Te Mo Te 4.369467438 78 | Mo Se W S W Te 4.1335319728 79 | Mo S W Se Mo S 10.4017902227 80 | Mo S W S W Te 5.06536061334 81 | Mo Se Mo Se W Se 12.0355114509 82 | Mo S Mo Se Mo Se 10.2633990221 83 | Mo S Mo Te W Te 3.22016362489 84 | Mo S W S Mo S 5.76220096775 85 | Mo S W Se W S 12.2109622027 86 | Mo S W S W Se 11.452835788 87 | W S Mo Te W Te 3.03321195825 88 | Mo S Mo S W S 5.63268967816 89 | Mo S W Te W Te 5.55545790452 90 | Mo S Mo Se Mo S 8.06469455265 91 | Mo Te Mo S W S 2.03829388143 92 | Mo S Mo Te Mo Se 1.43421839049 93 | Mo S Mo Te W S 2.3301063013 94 | Mo S W Se Mo Te 2.70862645565 95 | Mo S Mo S W Se 11.4426061588 96 | Mo S Mo Te Mo Te 1.94549825402 97 | Mo S W S Mo Se 8.40981688236 98 | Mo S Mo Se W Se 11.8136170115 99 | Mo S Mo Se W S 8.70079649785 100 | Mo S W Te Mo Se 2.30606742134 101 | Mo S Mo Te Mo S 0.622048532473 102 | Mo S Mo S Mo Se 8.68722345333 103 | Mo S Mo Se Mo Te 3.44812826807 104 | Mo S Mo S W Te 2.92875343096 105 | Mo S Mo S Mo Te 1.99487076172 106 | Mo Se Mo S W S 8.71734774607 107 | Mo S Mo Se W Te 5.40152117902 108 | Mo S W S W S 5.9995020822 109 | W Se W S W Te 4.11849695486 110 | W Se W Se W Te 12.5179577841 111 | W Se W Te W Se 11.4812604886 112 | W Se W Te W Te 11.424019865 113 | W Te Mo Te W Te 14.5392077246 114 | W Te Mo S W Te 1.58443956628 115 | W Te W Se W Te 11.9869880946 116 | W Te W S W Te 1.98858033426 117 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Active Learning for Accelerated Design of Layered Materials 2 | Bassman, L., Rajak, P., et al., *npj Computational Materials* **4**, 74 (2018). 3 | 4 | 5 | 6 | 7 | Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for thermoelectric devices of the future. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, however, screening of properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. This repository includes Gaussian process regression code (predict_maxval.py, predict_structure.py) for prediction of band gap, conduction band minimum dispersion curve, valance band maximum dispersion curve, and Thermoelectic EFF vs. dopant concentration curve. It also includes Bayesian optimization code (Bayesian_opt.py) to predict the best thermoelectric material using the fewest structure calculations. Data sets for all codes are also included. Desciptions of these codes and datasets, as well as how to run the code, are given below. Python code (snl_prep.py) is also included for automatically generating a structure file for a given layered material and uploading it to the Materials Project Database, along with bash scripts (create-[X]layers.sh) for automatically generating and uploading all **unique** 2-, 3-, and 4-layered materials using 'snl_prep.py'. 8 | 9 |
10 | 11 | #### 1 ```predict_maxval.py:``` 12 | *A Gaussian Process Regression model to predict band gap.* 13 | 14 | predict_maxval.py: creates a gaussian process regression model using x% of data from 3-layer-band_gap.txt. After buiding the model predcits the band gap of the remaining (1-x%) data as test set.
15 | 16 | *Dataset*
17 | ```3-layer-band_gap.txt``` CBM and VBM 3-layer heretero-structure
18 | ```xaxisvalue.txt``` Contains the value of the wave vector 19 | 20 | *Input Paramaters:*
21 | inputfile_name="3-layer-band_gap.txt" * ###file name of the input data *
22 | train_test_split=0.60 * ###split between training and test set*
23 | Nrun = 1
24 | 25 | To run the program: ```predict_maxval.py ```
26 | 27 | #### 2 ```predict_structure.py:``` 28 | *A Gaussian Process Regression model to predict conduction band maxima, valance band minima and Thermoelectic EFF function.* 29 | 30 | predict_structure.py build a gaussian process (GP) regression model for condunction band minima and valance band maximum. It takes 3-layer-band_structure.txt as inout data and split in into training and test set. GP model is build using training set and the images of prediced condunction band minima and valance band maximum of the test set is saved in folder Bandstructure. 31 | 32 | *Dataset*
33 | ```"3-layer-band_structure.txt``` Band gap for 3-layer heretero-structure
34 | 35 | *Input Paramaters:*
36 | inputfile_name="3-layer-band_structure.txt" * ###file name of the input data *
37 | train_test_split=0.60 * ###split between training and test set*
38 | Nrun = 1
39 | 40 | To run the program: ```predict_structure.py ```
41 | 42 | #### 3 ```Bayesian_opt.py:``` 43 | *An active learning model, based on Bayesian Optimization, to discover material with optimal property witn minimum structure evalulation* 44 | 45 | *Dataset*
46 | ```N_doped_EFF_max.txt ``` EFF (Electronic fitness function) maximum value for n-doped 3-layer heretero-structure
47 | ```P_doped_EFF_max.txt ``` EFF (Electronic fitness function) maximum value for p-doped 3-layer heretero-structure
48 | ```3-layer-band_gap.txt ``` Maximum Band gap for 3-layer heretero-structure
49 | 50 | *Input Paramaters:*
51 | Inside the code Bayesian_opt.py, we have
52 | Nruns = 1 * ### total number of Bayesian Optimization runs*
53 | train_test_split=0.10 * ### initial sampled data in a given Bayesian Optimization run*
54 | 55 | 56 | *To find n-doped 3-ayer hetero-structure with optimal EFF value*
57 | Run ```python3.6 Bayesian_opt.py N_doped_EFF_max ```
58 | *To find p-doped 3-ayer hetero-structure with optimal EFF value*
59 | Run ```python3.6 Bayesian_opt.py P_doped_EFF_max ```
60 | *To find maximum band gap*
61 | Run ```python3.6 Bayesian_opt.py 3-layer-band_gap.txt```
62 | 63 | 64 | #### 4 ```snl_prep.py:``` 65 | *Given a layered crystal structure as a string, the python script generates a structure file and uploads it to the Materials Project Database* 66 | 67 | For example, for the 4-layered heterostructure MoTe2-MoS2-WS2-WTe2, type: 68 | 69 | python snl_prep.py -s MoTe2-MoS2-WS2-WTe2 -d . 70 | 71 | The -s parameter is the structure of the layers encoded in a string. It must be formatted by the above way, but it could be any number of layers instead of just 4. The -d parameter specifies the base directory in which the newly created structure file should be placed, either absolute or relative to the pwd. 72 | 73 | #### 5 ```create-[X]layers.sh:``` 74 | *Bash code for automatically generating all **unique** 2-, 3-, and 4-layered TMDC heterostructures and uploading them to MP Database by repeated use of the above python code, snl_prep.py. snl_prep.py is assumed to be in the same directory as the bash script* 75 | 76 | To run, type one of the following: 77 | 78 | bash create-2layers.sh 79 |
80 | bash create-3layers.sh 81 |
82 | bash create-4layers.sh 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | -------------------------------------------------------------------------------- /create-2layers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | for M1 in Mo W 4 | do 5 | for X1 in S Se Te 6 | do 7 | for M2 in Mo W 8 | do 9 | for X2 in S Se Te 10 | do 11 | echo $M1${X1}2-$M2${X2}2 12 | python snl_prep.py -s $M1${X1}2-$M2${X2}2 -d . 13 | done 14 | done 15 | done 16 | done 17 | -------------------------------------------------------------------------------- /create-3layers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | i=0 3 | submitted=0 4 | for M1 in Mo W 5 | do 6 | for X1 in S Se Te 7 | do 8 | for M2 in Mo W 9 | do 10 | for X2 in S Se Te 11 | do 12 | for M3 in Mo W 13 | do 14 | for X3 in S Se Te 15 | do 16 | symm_index=0 17 | echo $M1${X1}2-$M2${X2}2-$M3${X3}2 18 | third=${M3}${X3} 19 | second=${M2}${X2} 20 | first=${M1}${X1} 21 | if [[ $first == MoS ]] 22 | then 23 | symm_index=$((symm_index+0)) 24 | elif [[ $first == MoSe ]] 25 | then 26 | symm_index=$((symm_index+1)) 27 | elif [[ $first == MoTe ]] 28 | then 29 | symm_index=$((symm_index+2)) 30 | elif [[ $first == WS ]] 31 | then 32 | symm_index=$((symm_index+3)) 33 | elif [[ $first == WSe ]] 34 | then 35 | symm_index=$((symm_index+4)) 36 | else 37 | symm_index=$((symm_index+5)) 38 | fi 39 | if [[ $second == MoS ]] 40 | then 41 | symm_index=$((symm_index+0)) 42 | elif [[ $second == MoSe ]] 43 | then 44 | symm_index=$((symm_index+6)) 45 | elif [[ $second == MoTe ]] 46 | then 47 | symm_index=$((symm_index+12)) 48 | elif [[ $second == WS ]] 49 | then 50 | symm_index=$((symm_index+18)) 51 | elif [[ $second == WSe ]] 52 | then 53 | symm_index=$((symm_index+24)) 54 | else 55 | symm_index=$((symm_index+30)) 56 | fi 57 | if [[ $third == MoS ]] 58 | then 59 | symm_index=$((symm_index+0)) 60 | elif [[ $third == MoSe ]] 61 | then 62 | symm_index=$((symm_index+36)) 63 | elif [[ $third == MoTe ]] 64 | then 65 | symm_index=$((symm_index+72)) 66 | elif [[ $third == WS ]] 67 | then 68 | symm_index=$((symm_index+108)) 69 | elif [[ $third == WSe ]] 70 | then 71 | symm_index=$((symm_index+144)) 72 | else 73 | symm_index=$((symm_index+180)) 74 | fi 75 | echo "i = "$i" symm_index = "$symm_index 76 | if (( $i > $symm_index )) 77 | then 78 | echo "already submitted "$M1${X1}2-$M2${X2}2-$M3${X3}2 79 | else 80 | echo "submitting "$M1${X1}2-$M2${X2}2-$M3${X3}2 81 | submitted=$((submitted+1)) 82 | python snl_prep.py -s $M1${X1}2-$M2${X2}2-$M3${X3}2 -d . 83 | fi 84 | i=$((i+1)) 85 | done 86 | done 87 | done 88 | done 89 | done 90 | done 91 | echo $submitted 92 | -------------------------------------------------------------------------------- /create-4layers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | i=0 3 | submitted=0 4 | for M1 in Mo W 5 | do 6 | for X1 in S Se Te 7 | do 8 | for M2 in Mo W 9 | do 10 | for X2 in S Se Te 11 | do 12 | for M3 in Mo W 13 | do 14 | for X3 in S Se Te 15 | do 16 | for M4 in Mo W 17 | do 18 | for X4 in S Se Te 19 | do 20 | symm_index=0 21 | fourth=${M4}${X4} 22 | third=${M3}${X3} 23 | second=${M2}${X2} 24 | first=${M1}${X1} 25 | if [[ $first == MoS ]] 26 | then 27 | symm_index=$((symm_index+0)) 28 | elif [[ $first == MoSe ]] 29 | then 30 | symm_index=$((symm_index+1)) 31 | elif [[ $first == MoTe ]] 32 | then 33 | symm_index=$((symm_index+2)) 34 | elif [[ $first == WS ]] 35 | then 36 | symm_index=$((symm_index+3)) 37 | elif [[ $first == WSe ]] 38 | then 39 | symm_index=$((symm_index+4)) 40 | else 41 | symm_index=$((symm_index+5)) 42 | fi 43 | if [[ $second == MoS ]] 44 | then 45 | symm_index=$((symm_index+0)) 46 | elif [[ $second == MoSe ]] 47 | then 48 | symm_index=$((symm_index+6)) 49 | elif [[ $second == MoTe ]] 50 | then 51 | symm_index=$((symm_index+12)) 52 | elif [[ $second == WS ]] 53 | then 54 | symm_index=$((symm_index+18)) 55 | elif [[ $second == WSe ]] 56 | then 57 | symm_index=$((symm_index+24)) 58 | else 59 | symm_index=$((symm_index+30)) 60 | fi 61 | if [[ $third == MoS ]] 62 | then 63 | symm_index=$((symm_index+0)) 64 | elif [[ $third == MoSe ]] 65 | then 66 | symm_index=$((symm_index+36)) 67 | elif [[ $third == MoTe ]] 68 | then 69 | symm_index=$((symm_index+72)) 70 | elif [[ $third == WS ]] 71 | then 72 | symm_index=$((symm_index+108)) 73 | elif [[ $third == WSe ]] 74 | then 75 | symm_index=$((symm_index+144)) 76 | else 77 | symm_index=$((symm_index+180)) 78 | fi 79 | if [[ $fourth == MoS ]] 80 | then 81 | symm_index=$((symm_index+0)) 82 | elif [[ $fourth == MoSe ]] 83 | then 84 | symm_index=$((symm_index+216)) 85 | elif [[ $fourth == MoTe ]] 86 | then 87 | symm_index=$((symm_index+432)) 88 | elif [[ $fourth == WS ]] 89 | then 90 | symm_index=$((symm_index+648)) 91 | elif [[ $fourth == WSe ]] 92 | then 93 | symm_index=$((symm_index+864)) 94 | else 95 | symm_index=$((symm_index+1080)) 96 | fi 97 | if (( $i > $symm_index )) 98 | then 99 | echo "already submitted "$M1${X1}2-$M2${X2}2-$M3${X3}2-$M4${X4}2 100 | else 101 | echo "submitting "$M1${X1}2-$M2${X2}2-$M3${X3}2-$M4${X4}2 102 | submitted=$((submitted+1)) 103 | #python snl_prep.py -s $M1${X1}2-$M2${X2}2-$M3${X3}2-$M4${X4}2 -d . 104 | fi 105 | i=$((i+1)) 106 | done 107 | done 108 | done 109 | done 110 | done 111 | done 112 | done 113 | done 114 | echo $submitted 115 | -------------------------------------------------------------------------------- /predict_maxval.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from matplotlib import pyplot as plt 3 | from sklearn.gaussian_process import GaussianProcessRegressor 4 | from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C ,WhiteKernel as Wht,Matern as matk 5 | from sklearn.gaussian_process.kernels import RationalQuadratic as expker 6 | from sklearn.metrics import mean_squared_error as MSError 7 | 8 | 9 | inputmap=dict() 10 | ninputmap=dict() 11 | totfea_atom=2 #total number of atoms per layer 12 | n_3layer_atoms=6 # number of atoms in 3 layer 13 | natom_layer=n_3layer_atoms*totfea_atom #total number of features 14 | 15 | #input parameters 16 | inputfile_name="3-layer-band_gap.txt" #file name of the input data 17 | train_test_split=0.60 #split between training and test set 18 | Nrun = 1 19 | 20 | #create input feature vector of the given n-layer heterostructure 21 | def createinputmap(inputmap,ninputmap,totfea_atom): 22 | #define the eletronegetivity and ionization potential of each atoms 23 | inputmap['Mo'] = [2.16,684.3] 24 | inputmap['W'] = [2.36,770.0] 25 | inputmap['S'] = [2.58,999.6] 26 | inputmap['Se'] = [2.55,941.0] 27 | inputmap['Te'] = [2.10,869.3] 28 | 29 | #normalize the input features by (tt-xmax)/(xmax-xmin) 30 | Xmax = np.empty(totfea_atom,dtype=float) 31 | Xmin = np.empty(totfea_atom, dtype=float) 32 | Xmean= np.empty(totfea_atom,dtype=float) 33 | Xstd = np.empty(totfea_atom,dtype=float) 34 | Xmax.fill(0.0) 35 | Xmin.fill(10000.0) 36 | Xmean.fill(0.0) 37 | Xstd.fill(0.0) 38 | nfeatures=0 39 | for keys in inputmap: 40 | nfeatures+=1 41 | for ii in range(0,totfea_atom): 42 | if Xmax[ii] < inputmap[keys][ii]: Xmax[ii]=inputmap[keys][ii] 43 | if Xmin[ii] > inputmap[keys][ii]: Xmin[ii]=inputmap[keys][ii] 44 | Xmean[ii]+=inputmap[keys][ii] 45 | for ii in range(0,totfea_atom): 46 | Xmean[ii]=Xmean[ii]/float(nfeatures) 47 | for keys in inputmap: 48 | for ii in range(0, totfea_atom): 49 | Xstd[ii]+=(inputmap[keys][ii]- Xmean[ii])*(inputmap[keys][ii]- Xmean[ii]) 50 | for ii in range(0, totfea_atom): 51 | Xstd[ii]=np.sqrt(Xstd[ii]/float(nfeatures)) 52 | print("Xmax and Xmin: ",Xmax,Xmin) 53 | print("Xmean and Xstd: ",Xmean,Xstd) 54 | for keys in inputmap: 55 | ninputmap[keys]=list() 56 | for ii in range(0, totfea_atom): 57 | ninputmap[keys].append((inputmap[keys][ii]-Xmean[ii])/Xstd[ii]) 58 | #print the final keys: 59 | # for keys in inputmap: 60 | # print("key :", keys,inputmap[keys]) 61 | # for keys in ninputmap: 62 | # print("nkey :", keys, ninputmap[keys]) 63 | 64 | 65 | #read input data 66 | def readinput(filename,natom_layer): 67 | inputfile=open(filename,'r') 68 | dataset=list() 69 | itag=0 70 | count=-1 71 | ndata=0 72 | for lines in inputfile: 73 | if itag==0: 74 | ndata=int(lines) 75 | Xdata = np.ndarray(shape=(ndata, natom_layer), dtype=float) 76 | Ydata = np.empty(ndata,dtype=float) 77 | itag=1 78 | else : 79 | lines = lines.replace("\n", "").split() 80 | count+=1 81 | for ii in range(0,lines.__len__()-1): 82 | jj=lines[ii] 83 | Xdata[count][2 * ii] = ninputmap[jj][0] 84 | Xdata[count][2 * ii + 1] = ninputmap[jj][1] 85 | Ydata[count] = float(lines[lines.__len__() - 1]) 86 | 87 | #print the entire dataset 88 | # for ii in range(0,ndata): 89 | # print("data: ",ii,Xdata[ii][:],Ydata[ii]) 90 | return Xdata,Ydata,ndata 91 | 92 | #building a gaussian process regression model 93 | def gpregression(Xtrain,Ytrain,Xtest,Ytest,ntrain,ntest): 94 | print("regression") 95 | cmean=[1.0]*12 96 | cbound=[[1e-3, 1000]]*12 97 | kernel = C(1.0, (1e-3, 1e3)) * matk(cmean, cbound, 1.5)+ Wht(1.0, (1e-3, 1e3)) 98 | # kernel = C(1.0, (1e-3, 1e3)) * matk(1, (1e-05, 1000.0), 2.5) + Wht(1.0, (1e-3, 1e3))+ C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2)) 99 | 100 | gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=100, normalize_y=False) 101 | gp.fit(Xtrain, Ytrain) 102 | print("initial parameters:", kernel) 103 | print("optimal parameters:", gp.kernel_, "likelihood:", gp.log_marginal_likelihood(gp.kernel_.theta)) 104 | y_pred, sigma = gp.predict(Xtest, return_std=True) 105 | dataorder=np.argsort(Ytest) 106 | tYest=Ytest[dataorder] 107 | ty_pred=y_pred[dataorder] 108 | tsigma=sigma[dataorder] 109 | del Ytest,y_pred,sigma 110 | Ytest=tYest 111 | y_pred=ty_pred 112 | sigma=tsigma 113 | toterr=0.0 114 | for val in range(0,ntest): 115 | # print("Prediction: ",Ytest[val]," ",y_pred[val]," ",sigma[val]) 116 | toterr+=np.abs(Ytest[val]-y_pred[val]) 117 | print("toterr prediction loss : ",toterr,toterr/float(ntest)) 118 | fig = plt.figure(figsize=(14,10)) 119 | plt.rc('xtick', labelsize=20) 120 | plt.rc('ytick', labelsize=20) 121 | plt.rc('font', weight='bold') 122 | xxdummy=range(ntest) 123 | plt.plot(xxdummy, Ytest, 'r-', linewidth=3.5, label=u'True Value') 124 | plt.plot(xxdummy, Ytest, 'r.', markersize=20) 125 | plt.plot(xxdummy, y_pred, 'b--', linewidth=3.5, label=u'Prediction') 126 | plt.plot(xxdummy, y_pred, 'b.', markersize=20) 127 | plt.fill(np.concatenate([xxdummy, xxdummy[::-1]]),np.concatenate([y_pred - 1.9600 * sigma,(y_pred + 1.9600 * sigma)[::-1]]),alpha=.5, fc='y', ec='None', label='95% confidence interval') 128 | plt.xlabel('tri-layer structure',fontsize=40, fontweight='bold') 129 | plt.ylabel('Band GAP',fontsize=40, fontweight='bold') 130 | plt.legend(loc='upper left', ncol=1, fancybox=True, shadow=True, prop={'size': 20}) 131 | # plt.legend(loc='upper left') 132 | plt.title("TEST DATA",fontsize=40,fontweight='bold') 133 | #-----training set----- 134 | yt_pred, tsigma = gp.predict(Xtrain, return_std=True) 135 | # for val in range(0,ntrain): 136 | # print("Training set: ",Ytrain[val]," ",yt_pred[val]," ",tsigma[val]) 137 | print("Total training errror: ",np.sqrt(MSError(Ytrain,yt_pred))) 138 | print("Total prediction errror: ", np.sqrt(MSError(Ytest,y_pred))) 139 | # xxtdummy=range(ntrain) 140 | # plt.plot(xxtdummy, Ytrain, 'r-', markersize=10, label=u'Observations') 141 | # plt.plot(xxtdummy, Ytrain, 'r.', markersize=10) 142 | # plt.plot(xxtdummy, yt_pred, 'b-', markersize=10, label=u'Prediction') 143 | # plt.plot(xxtdummy, yt_pred, 'b.', markersize=10) 144 | # plt.fill(np.concatenate([xxtdummy, xxtdummy[::-1]]),np.concatenate([yt_pred - 1.9600 * tsigma,(yt_pred + 1.9600 * tsigma)[::-1]]),alpha=.8, fc='b', ec='None', label='95% confidence interval') 145 | # plt.xlabel('$x$') 146 | # plt.ylabel('$f(x)$') 147 | # plt.legend(loc='upper left') 148 | # plt.title("Training data") 149 | plt.ylim(-0.6,1.6) 150 | plt.show() 151 | # plt.savefig('fig1a.png') 152 | # plt.close() 153 | return 154 | 155 | 156 | 157 | #------- Main Program ------------- 158 | 159 | createinputmap(inputmap,ninputmap,totfea_atom) 160 | Xdata,Ydata,ndata=readinput(inputfile_name,natom_layer) 161 | 162 | print("Original Training and Y :",np.shape(Xdata),np.shape(Ydata)) 163 | print("Transpose Training and Y : ",np.shape(np.transpose(Xdata)),np.shape(np.transpose(Ydata))) 164 | print("Original Training and Y :",np.shape(Xdata),np.shape(Ydata)) 165 | 166 | ntrain=int(train_test_split*ndata) 167 | ntest=ndata-ntrain 168 | print("Total training and Test Data: ",ntrain,ntest) 169 | for ii in range(0,Nrun): 170 | dataset=np.random.permutation(ndata) 171 | a1data=np.empty(ntrain, dtype=int) 172 | a2data=np.empty(ntest, dtype=int) 173 | a1data[:]=dataset[0:ntrain] 174 | a2data[:]=dataset[ntrain:ndata] 175 | Xtrain=np.ndarray(shape=(ntrain, natom_layer), dtype=float) 176 | Ytrain = np.empty(ntrain, dtype=float) 177 | Xtest = np.ndarray(shape=(ntest, natom_layer), dtype=float) 178 | Ytest = np.empty(ntest, dtype=float) 179 | for itrain in range(0,ntrain): 180 | mm=a1data[itrain] 181 | Xtrain[itrain][:]=Xdata[mm][:] 182 | Ytrain[itrain]=Ydata[mm] 183 | for itest in range(0,ntest): 184 | mm = a2data[itest] 185 | Xtest[itest][:]=Xdata[mm][:] 186 | Ytest[itest]=Ydata[mm] 187 | gpregression(Xtrain,Ytrain,Xtest,Ytest,ntrain,ntest) 188 | del Xtrain,Ytrain 189 | del Xtest,Ytest 190 | 191 | -------------------------------------------------------------------------------- /predict_structure.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from matplotlib import pyplot as plt 3 | from sklearn.gaussian_process import GaussianProcessRegressor 4 | from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C ,WhiteKernel as Wht,Matern as matk 5 | from sklearn.gaussian_process.kernels import RationalQuadratic as expker 6 | from sklearn.metrics import mean_squared_error as MSError 7 | from scipy.stats import norm 8 | from base64 import b16encode 9 | import warnings 10 | warnings.filterwarnings("ignore") 11 | 12 | inputmap=dict() 13 | ninputmap=dict() 14 | totfea_atom=2 #total number of atoms per layer 15 | Natomsinpayer=6 # number of atoms in 3 layer 16 | natom_layer=Natomsinpayer*totfea_atom #total number of features 17 | Nbandpoint=30 # number of points at which GP model is build 18 | 19 | #input paramters 20 | inputfile_name="3-layer-band_structure.txt" #file name of the input data 21 | train_test_split=0.60 #split between training and test set 22 | Nruns = 1 23 | 24 | #create input feature vector of the given n-layer heterostructure 25 | def createinputmap(inputmap,ninputmap,totfea_atom): 26 | #define the eletronegetivity and ionization potential of each atoms 27 | inputmap['Mo'] = [2.16,684.3,190.0] 28 | inputmap['W'] = [2.36,770.0,193.0] 29 | inputmap['S'] = [2.58,999.6,88.8] 30 | inputmap['Se'] = [2.55,941.0,103.0] 31 | inputmap['Te'] = [2.10,869.3,123.0] 32 | 33 | #normalize the input features by (tt-xmax)/(xmax-xmin) 34 | Xmax = np.empty(totfea_atom,dtype=float) 35 | Xmin = np.empty(totfea_atom, dtype=float) 36 | Xmean= np.empty(totfea_atom,dtype=float) 37 | Xstd = np.empty(totfea_atom,dtype=float) 38 | Xmax.fill(0.0) 39 | Xmin.fill(10000.0) 40 | Xmean.fill(0.0) 41 | Xstd.fill(0.0) 42 | nfeatures=0 43 | for keys in inputmap: 44 | nfeatures+=1 45 | for ii in range(0,totfea_atom): 46 | if Xmax[ii] < inputmap[keys][ii]: Xmax[ii]=inputmap[keys][ii] 47 | if Xmin[ii] > inputmap[keys][ii]: Xmin[ii]=inputmap[keys][ii] 48 | Xmean[ii]+=inputmap[keys][ii] 49 | for ii in range(0,totfea_atom): 50 | Xmean[ii]=Xmean[ii]/float(nfeatures) 51 | for keys in inputmap: 52 | for ii in range(0, totfea_atom): 53 | Xstd[ii]+=(inputmap[keys][ii]- Xmean[ii])*(inputmap[keys][ii]- Xmean[ii]) 54 | for ii in range(0, totfea_atom): 55 | Xstd[ii]=np.sqrt(Xstd[ii]/float(nfeatures)) 56 | print("Xmax and Xmin: ",Xmax,Xmin) 57 | print("Xmean and Xstd: ",Xmean,Xstd) 58 | for keys in inputmap: 59 | ninputmap[keys]=list() 60 | for ii in range(0, totfea_atom): 61 | ninputmap[keys].append((inputmap[keys][ii]-Xmin[ii])/(Xmax[ii]-Xmin[ii])) # normalized by by (tt-xmax)/(xmax-xmin) 62 | 63 | #read input data 64 | def readinput(filename,natom_layer,Natomsinpayer,Nbandpoint): 65 | inputfile=open(filename,'r') 66 | itag=0 67 | count=-1 68 | ndata=0 69 | for lines in inputfile: 70 | if itag==0: 71 | ndata=int(lines) 72 | Xdata = np.ndarray(shape=(ndata, natom_layer), dtype=float) 73 | Xinfo = np.chararray(ndata, itemsize=20) 74 | Ytopdata = np.ndarray(shape=(ndata,Nbandpoint),dtype=float) 75 | Ybotdata = np.ndarray(shape=(ndata, Nbandpoint), dtype=float) 76 | itag=1 77 | else : 78 | lines = lines.replace("\n", "").split() 79 | structname=str() 80 | count+=1 81 | for ii in range(0,Natomsinpayer): 82 | jj=lines[ii] 83 | if (ii >0): structname = structname + '-' + jj 84 | if(ii==0): structname=jj 85 | Xdata[count][2 * ii] = ninputmap[jj][0] 86 | Xdata[count][2 * ii + 1] = ninputmap[jj][1] 87 | Xinfo[count]=structname 88 | for itop in range(Natomsinpayer,Natomsinpayer+Nbandpoint): 89 | Ytopdata[count][itop-Natomsinpayer]=float(lines[itop]) 90 | for ibot in range(Natomsinpayer+Nbandpoint,Natomsinpayer+Nbandpoint+Nbandpoint): 91 | Ybotdata[count][ibot - Natomsinpayer-Nbandpoint] = float(lines[ibot]) 92 | # print("structname: ",structname,lines) 93 | # print("datatop : ",lines[Natomsinpayer:Natomsinpayer+Nbandpoint]) 94 | # print("databottom: ",lines[Natomsinpayer+Nbandpoint:lines.__len__()]) 95 | return Xdata,Ytopdata,Ybotdata,Xinfo,ndata 96 | 97 | #read x-axis value of the 30 points at which gp model is build 98 | def readxaxisval(filename,Nbandpoint): 99 | inputfile = open(filename, 'r') 100 | Xdata = np.empty(Nbandpoint, dtype=float) 101 | count=-1 102 | # Xdata=range(0,Nbandpoint) 103 | for val in inputfile: 104 | count+=1 105 | Xdata[count]=float(val) 106 | return Xdata 107 | 108 | def plotbandstructure(XX,structure,YY1,YY2): 109 | fig = plt.figure(figsize=(7, 7)) 110 | plt.rc('xtick', labelsize=20) 111 | plt.rc('ytick', labelsize=20) 112 | plt.rc('font', weight='bold') 113 | plt.plot(XX, YY1, 'b-', linewidth=3.5, label=u'HOMO') 114 | plt.plot(XX, YY2, 'r-', linewidth=3.5, label=u'LOMO') 115 | plt.title(structure, fontsize=20, fontweight='bold') 116 | plt.show() 117 | 118 | #make polt of the CBM/VBM for the test set inside a folder Bandstructure 119 | def plotbandtest(XX,structure,YY1true,YY2true,YY1predict,YY2predict,YY1sigma,YY2sigma,myid): 120 | fig = plt.figure(figsize=(10,10)) 121 | plt.rc('xtick', labelsize=20) 122 | plt.rc('ytick', labelsize=20) 123 | plt.rc('font', weight='bold') 124 | plt.plot(XX, YY1true, 'b-', linewidth=3.5, label=u'CBM-Ground-Truth') 125 | plt.plot(XX, YY1predict, 'c--', linewidth=3.5) 126 | #plt.fill(np.concatenate([XX, XX[::-1]]),np.concatenate([YY1predict - 1.9600 * YY1sigma, (YY1predict + 1.9600 * YY1sigma)[::-1]]), alpha=.3, fc='y', ec='None',label='95% confidence interval') 127 | plt.plot(XX, YY2true, 'r-', linewidth=3.5, label=u'VBM-Ground-Truth') 128 | plt.plot(XX, YY2predict, 'm--', linewidth=3.5) 129 | #plt.fill(np.concatenate([XX, XX[::-1]]),np.concatenate([YY2predict - 1.9600 * YY2sigma, (YY2predict + 1.9600 * YY2sigma)[::-1]]), alpha=.3, fc='g',ec='None', label='95% confidence interval') 130 | plt.title(structure, fontsize=20, fontweight='bold') 131 | plt.legend(loc='upper right', bbox_to_anchor=(0.28, 1.16), ncol=1, fancybox=True, shadow=True, prop={'size': 14}) 132 | plt.xlabel('Wave Vector',fontsize=20, fontweight='bold') 133 | plt.ylabel('Energy(eV)',fontsize=20, fontweight='bold') 134 | imagefile = "Bandstructure/Strucuture" + str(myid+1) 135 | plt.savefig(imagefile) 136 | # plt.show() 137 | 138 | #Build GP regression model 139 | def gpregression(Xtrain,Ytrain,Nfeature): 140 | cmean=[1.0]*Nfeature 141 | cbound=[[1e-3, 10000]]*Nfeature 142 | # kernel = C(1.0, [1e-3, 1e3]) * RBF(cmean, cbound) 143 | kernel = C(1.0, (1e-3, 1e3)) * matk(cmean, cbound, 2.5)+ Wht(1.0, (1e-3, 1e3)) 144 | 145 | gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=50, normalize_y=False) 146 | gp.fit(Xtrain, Ytrain) 147 | print("initial parameters:", kernel) 148 | print("optimal parameters A:", gp.kernel_, "likelihood:", gp.log_marginal_likelihood(gp.kernel_.theta)) 149 | # print("likelihood:", gp.log_marginal_likelihood(gp.kernel_.theta)) 150 | return gp 151 | 152 | def gprediction(gpnetwork,xtest): 153 | y_pred, sigma = gpnetwork.predict(xtest, return_std=True) 154 | return y_pred, sigma 155 | 156 | #------- Program Starts from here ------------- 157 | createinputmap(inputmap,ninputmap,totfea_atom) 158 | Xdata,Ytopdata,Ybotdata,Xinfo,ndata=readinput(inputfile_name,natom_layer,Natomsinpayer,Nbandpoint) 159 | xaxisval=readxaxisval("xaxisvalue.txt",Nbandpoint) 160 | 161 | for ii in range(0,ndata): 162 | materialname = Xinfo[ii].decode('utf-8') 163 | print("Xinfo: ",materialname) 164 | # plotbandstructure(xaxisval,materialname,Ytopdata[ii,:].ravel(),Ybotdata[ii,:].ravel()) 165 | 166 | # Make a regression model for each of the 60 points 167 | ntrain=int(train_test_split*ndata) 168 | ntest=ndata-ntrain 169 | print("Total training and Test Data: ",ntrain,ntest) 170 | for ii in range(0,Nruns): 171 | dataset = np.random.permutation(ndata) 172 | a1data=np.empty(ntrain, dtype=int) 173 | a2data=np.empty(ntest, dtype=int) 174 | a1data[:]=dataset[0:ntrain] 175 | a2data[:]=dataset[ntrain:ndata] 176 | #create the training set 177 | Xtrain = np.ndarray(shape=(ntrain, natom_layer), dtype=float) 178 | Xtraininfo = np.chararray(ntrain, itemsize=20) 179 | Ytoptrain = np.ndarray(shape=(ntrain, Nbandpoint), dtype=float) 180 | Ybottrain = np.ndarray(shape=(ntrain, Nbandpoint), dtype=float) 181 | Xtrain[0:ntrain, :] = Xdata[a1data, :] 182 | Xtraininfo[0:ntrain] = Xinfo[a1data] 183 | Ytoptrain[0:ntrain,:] = Ytopdata[a1data,:] 184 | Ybottrain[0:ntrain, :] = Ybotdata[a1data, :] 185 | #create the test set 186 | Xtest = np.ndarray(shape=(ntest, natom_layer), dtype=float) 187 | Xtestinfo = np.chararray(ntest, itemsize=20) 188 | Ytoptest = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 189 | Ytoppredict = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 190 | Ytopsigma = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 191 | Ybottest = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 192 | Ybotpredict = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 193 | Ybotsigma = np.ndarray(shape=(ntest, Nbandpoint), dtype=float) 194 | Xtest[0:ntest, :] = Xdata[a2data, :] 195 | Xtestinfo[0:ntest] = Xinfo[a2data] 196 | Ytoptest[0:ntest,:] = Ytopdata[a2data,:] 197 | Ybottest[0:ntest, :] = Ybotdata[a2data, :] 198 | gptopmodel=list() 199 | gpbotmodel=list() 200 | for jj in range(0,Nbandpoint): 201 | Ytemp1=Ytoptrain[:,jj] 202 | Ytemp2 = Ybottrain[:, jj] 203 | print("Point number: ",jj) 204 | gptemp1 = gpregression(Xtrain,Ytemp1.ravel(),natom_layer) 205 | gptemp2 = gpregression(Xtrain, Ytemp2.ravel(), natom_layer) 206 | gptopmodel.append(gptemp1) 207 | gpbotmodel.append(gptemp2) 208 | del gptemp1,gptemp2 209 | #Test the Model 210 | for jj in range(0,Nbandpoint): 211 | Ytoppredict[:,jj], Ytopsigma[:,jj] = gprediction(gptopmodel[jj], Xtest) 212 | Ybotpredict[:,jj], Ybotsigma[:,jj] = gprediction(gpbotmodel[jj], Xtest) 213 | #Make plots of the all the test cases 214 | for kk in range(0,ntest): 215 | materialname = Xtestinfo[kk].decode('utf-8') 216 | plotbandtest(xaxisval, materialname, Ytoptest[kk, :].ravel(), Ybottest[kk, :].ravel(),Ytoppredict[kk, :].ravel(), Ybotpredict[kk, :].ravel(),Ytopsigma[kk, :].ravel(), Ybotsigma[kk, :].ravel(),kk) 217 | -------------------------------------------------------------------------------- /snl_prep.py: -------------------------------------------------------------------------------- 1 | # 4/21/2017 Lindsay Bassman bassman@usc.edu 2 | import getopt, sys, os 3 | import shutil 4 | import numpy as np 5 | from pymatgen import Structure, Lattice 6 | from pymatgen.matproj.rest import MPRester 7 | from pymatgen.io.vasp.inputs import Poscar 8 | from pymatgen.transformations.site_transformations import RemoveSitesTransformation, ReplaceSiteSpeciesTransformation 9 | from pymatgen.transformations.standard_transformations import SupercellTransformation 10 | from pymatgen.matproj.snl import StructureNL 11 | from pymatgen import __version__ as pmgversion 12 | 13 | #elements in TMDCs 14 | elems = ["Mo", "W", "S", "Se", "Te"] 15 | elidx = {"Mo": 0, "W": 1, "S": 2, "Se": 3, "Te": 4} 16 | molec = ["MoS2", "MoSe2", "MoTe2", "WS2", "WSe2", "WTe2"] 17 | molidx = {"MoS2": 0, "MoSe2": 1, "MoTe2": 2, "WS2": 3, "WSe2": 4, "WTe2": 5} 18 | 19 | def invalid(reason): 20 | # Prints error message and exits. 21 | print("Invalid "+reason+".") 22 | sys.exit(2) 23 | 24 | # define the history node 25 | def history_node(t): 26 | pmg_github_url = 'https://github.com/materialsproject/pymatgen/tree/{}' 27 | return {'name': t.__module__ + "." + t.__class__.__name__, 28 | 'url': pmg_github_url.format(pmgversion), 29 | 'description': str(t)} 30 | 31 | #creat SNl for submission to MP database 32 | def create_SNL(dirbase, molecules, atoms, spc_present, num_each_spc, struct, s): 33 | layers = len(molecules) 34 | with MPRester("sm5RbuEp83T9Wo7P") as m: 35 | first_mol = struct[0] 36 | mono_or_homo = 0 37 | #if system is a monolayer or homogeneous use its proper .cif file, else use generic WTe2 for heterostructures 38 | if (layers == 1) or all(x==first_mol for x in struct): 39 | mono_or_homo = 1 40 | if (first_mol == molec[0]): 41 | structure = m.get_structure_by_material_id("mp-2815") #MoS2 42 | ref = m.get_materials_id_references("mp-2815") 43 | r1 = np.array([0,2,4]) 44 | elif (first_mol == molec[1]): 45 | structure = m.get_structure_by_material_id("mp-1634") #MoSe2 46 | ref = m.get_materials_id_references("mp-1634") 47 | r1 = np.array([0,2,4]) 48 | elif (first_mol == molec[2]): 49 | structure = m.get_structure_by_material_id("mp-602") #MoTe2 50 | ref = m.get_materials_id_references("mp-602") 51 | r1 = np.array([1,2,5]) 52 | elif (first_mol == molec[3]): 53 | structure = m.get_structure_by_material_id("mp-224") #WS2 54 | ref = m.get_materials_id_references("mp-224") 55 | r1 = np.array([0,3,5]) 56 | elif (first_mol == molec[4]): 57 | structure = m.get_structure_by_material_id("mp-1821") #WSe2 58 | ref = m.get_materials_id_references("mp-1821") 59 | r1 = np.array([0,2,4]) 60 | elif (first_mol == molec[5]): 61 | structure = m.get_structure_by_material_id("mp-1019322") #WTe2 62 | ref = m.get_materials_id_references("mp-1019322") 63 | r1 = np.array([0,3,5]) 64 | else: 65 | structure = m.get_structure_by_material_id("mp-1019322") #WTe2 66 | ref = m.get_materials_id_references("mp-1019322") 67 | r1 = np.array([0,3,5]) 68 | 69 | # initialize history 70 | history = [] 71 | 72 | #half the height of original unit cell...to be used for vacuum length calculation later 73 | halfz = (structure.lattice.c)/2 74 | 75 | #make supercell if necessary 76 | levels = layers 77 | if (levels%2 == 1): levels = levels+1 78 | tsuper = SupercellTransformation([[1,0,0],[0,1,0],[0,0,(levels)/2]]) 79 | history.append(history_node(tsuper)) 80 | supercell = tsuper.apply_transformation(structure) 81 | 82 | #make species replacements for heterostructures with more than one layer 83 | levels = layers 84 | if (levels%2 == 1): levels = levels+1 85 | #if heterostructure has more than one layer: 86 | if (mono_or_homo == 0): 87 | for i in range(0,len(molecules)): 88 | if (molecules[i] == 5): 89 | continue 90 | else: 91 | TMspc = elems[atoms[2*i]] 92 | TMloc = (levels*2) + (i%2)*(levels/2) + int(np.floor((i)/2)) 93 | DCspc = elems[atoms[2*i+1]] 94 | DCloc1 = (levels - (levels/2)) - i%2*(levels/2) + int(np.floor((i)/2)) 95 | DCloc2 = levels + i%2*(levels/2) + int(np.floor((i)/2)) 96 | t1 = ReplaceSiteSpeciesTransformation({TMloc:TMspc}) 97 | t2 = ReplaceSiteSpeciesTransformation({DCloc1:DCspc}) 98 | t3 = ReplaceSiteSpeciesTransformation({DCloc2:DCspc}) 99 | history.append(history_node(t1)) 100 | history.append(history_node(t2)) 101 | history.append(history_node(t3)) 102 | supercell = t1.apply_transformation(supercell) 103 | supercell = t2.apply_transformation(supercell) 104 | supercell = t3.apply_transformation(supercell) 105 | 106 | #remove top layer of atom if necessary 107 | mult_factor = (layers+1)/2 -1 108 | r = r1 + (r1+1)*mult_factor 109 | tremove = RemoveSitesTransformation(r) 110 | if (layers%2 == 1): 111 | supercell = tremove.apply_transformation(supercell) 112 | history.append(history_node(tremove)) 113 | 114 | #sort structure 115 | supercell = supercell.get_sorted_structure() 116 | 117 | #extend z-axis cell vector to add vaccuum to supercell 118 | vacuum = 10.0 119 | old_lattice = supercell.lattice 120 | if (layers%2 == 1): 121 | new_c = old_lattice.c - halfz + vacuum 122 | else: 123 | new_c = old_lattice.c + vacuum 124 | new_lattice = Lattice.from_parameters(old_lattice.a, old_lattice.b, new_c, old_lattice.alpha, old_lattice.beta, old_lattice.gamma) 125 | final_structure = Structure(new_lattice,supercell.species,supercell.frac_coords*np.array([1., 1., (old_lattice.c/new_lattice.c)]), coords_are_cartesian=False) 126 | hnode = {'name': 'add vaccuum', 'url': '','description': 'increase z-direction cell vector by 10 angstroms'} 127 | history.append(hnode) 128 | 129 | #creat final SNL 130 | authors = [{"name": "Lindsay Bassman", "email": "bassman@usc.edu"}] 131 | projects = ["TMDC-Heterostructures"] 132 | remarks = ["MAGICS calculation of band structures of 2D TMDC stacked heterostructures"] 133 | final_snl = StructureNL(final_structure, authors, projects=projects, remarks=remarks, references=ref, history=history) 134 | 135 | #optionally write POSCAR file 136 | poscar = Poscar(final_structure, s) 137 | poscar.write_file(dirbase+"POSCAR", direct=False) 138 | 139 | #submit snl 140 | #with MPRester("sm5RbuEp83T9Wo7P",endpoint="https://www.materialsproject.org/rest/v1") as m2: 141 | # m2.submit_snl(final_snl) 142 | 143 | def main(): 144 | # Parse command line arguments. 145 | try: 146 | opts, args = getopt.getopt(sys.argv[1:], "s:d:") 147 | except getopt.GetoptError as err: 148 | invalid(err) 149 | force = True 150 | for o, a in opts: 151 | if (o == "-s"): 152 | s = a 153 | elif (o == "-d"): 154 | dirbase = a 155 | struct = s.split("-") 156 | if len(struct) == 0: 157 | print("Invalid structure.") 158 | sys.exit(2) 159 | molecules = [0]* (len(struct)) 160 | atoms = [0]*(2*len(struct)) 161 | for i, elem in enumerate(struct): 162 | if elem not in molec: 163 | print("Invalid structure.") 164 | for j, mol in enumerate(molec): 165 | if mol == elem: 166 | molecules[i] = j 167 | atoms[2*i] = int(np.floor(j/3)) 168 | atoms[2*i+1] = j%3 + 2 169 | num_each_spc = [0]*5 170 | spc_present = [] 171 | for i, idx in enumerate(atoms): 172 | # Double count for every other element. 173 | num_each_spc[idx] += 1+i%2 174 | for idx, contains in enumerate(num_each_spc): 175 | if contains > 0: 176 | spc_present.append(idx) 177 | # Create folders. 178 | dirbase = s+"/" 179 | if not os.path.exists(dirbase): 180 | os.makedirs(dirbase) 181 | #create structure SNL 182 | create_SNL(dirbase, molecules, atoms, spc_present, num_each_spc, struct, s) 183 | 184 | if __name__ == "__main__": 185 | main() 186 | -------------------------------------------------------------------------------- /xaxisvalue.txt: -------------------------------------------------------------------------------- 1 | 0.0 2 | 0.1214651727 3 | 0.242930323536 4 | 0.364395496236 5 | 0.485860647072 6 | 0.607325819771 7 | 0.728790970607 8 | 0.850256143307 9 | 0.971721294143 10 | 1.09318646684 11 | 1.09318646684 12 | 1.16313733075 13 | 1.23308819465 14 | 1.30303905863 15 | 1.37298992253 16 | 1.44294078644 17 | 1.51289165034 18 | 1.58284251432 19 | 1.65279337823 20 | 1.72274424213 21 | 1.72274424213 22 | 1.8631142619 23 | 2.00348428166 24 | 2.14385428247 25 | 2.28422430224 26 | 2.42459432201 27 | 2.56496434178 28 | 2.70533434259 29 | 2.84570436235 30 | 2.98607438212 31 | --------------------------------------------------------------------------------