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Urban 2 | 3 | # List of Public Tools and Data for Atomistic Machine Learning 4 | 5 | **A Collection of Public Open-Source Tools and Databases for Atomistic Machine Learning** 6 | 7 | ## Table of Contents 8 | 9 | * [Contributing](#contributing) 10 | * [ML atomistic potentials](#ml-atomistic-potentials) 11 | * [ANN based potential implementations](#ann-based-potential-implementations) 12 | * [Other ML based potential implementations](#other-ml-based-potential-implementations) 13 | * [ML tools and packages for materials science and drug discovery applications](#ml-tools-and-packages-for-materials-science-and-drug-discovery-applications) 14 | * [Databases](#databases) 15 | * [General databases](#general-databases) 16 | * [Databases for inorganic materials](#databases-for-inorganic-materials) 17 | * [Databases for organic molecules and materials](#databases-for-organic-molecules-and-materials) 18 | * [Workflow management](#workflow-management) 19 | * [Peer-reviewed articles referring to this document](#peer-reviewed-articles-referring-to-this-document) 20 | 21 | ## Contributing 22 | 23 | We welcome everybody to contribute to this list. Your name will be added to the list of contributors at the top of this document. 24 | 25 | ## ML atomistic potentials 26 | 27 | ### ANN based potential implementations 28 | 29 | Entries sorted by the year of the publication. 30 | 31 | | Name | Features | Reference | 32 | | ---------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | 33 | | [ænet](http://ann.atomistic.net) | Capable of handling many chemical species | [Artrith, Urban, *Comput. Mater. Sci.* **114** (2016) 135](https://doi.org/10.1016/j.commatsci.2015.11.047) | 34 | | [Amp](https://bitbucket.org/andrewpeterson/amp) | Large descriptor library | [Khorshidi, Peterson, *Comput. Phys. Commun.* **207** (2016) 310](https://doi.org/10.1016/j.cpc.2016.05.010) | 35 | | [ANI](https://github.com/isayev/ASE_ANI) | Accurate potential for molecular systems | [Smith, Isayev, Roitberg, *Chem. Sci.* **8** (2017) 3192](https://doi.org/10.1039/C6SC05720A) | 36 | | [TensorMol](https://github.com/jparkhill/TensorMol) | Electrostatics and van der Waals interactions | [Yao et al., *Chem. Sci.* **9** (2018) 2261](https://doi.org/10.1039/C7SC04934J) | 37 | | [DeePMD-kit](https://github.com/deepmodeling/deepmd-kit) | GPU support | [Wang et al., *Comput. Phys. Commun.* **228** (2018) 178](https://doi.org/10.1016/j.cpc.2018.03.016) | 38 | | [SchNetPack](https://github.com/atomistic-machine-learning/schnetpack) | Feature learning | [Schütt et al., *J. Chem. Theory Comput.* **15** (2019) 448](https://doi.org/10.1021/acs.jctc.8b00908) | 39 | | [N2P2](https://compphysvienna.github.io/n2p2) | Behler-Parinello neural network potential | [Singraber et al., *J. Chem. Theory Comput.* **15** (2019) 1827](https://doi.org/10.1021/acs.jctc.8b00770) | 40 | | [SchNarc](https://github.com/schnarc/SchNarc) | Extension to multiple electronic states based on [SchNet](https://github.com/atomistic-machine-learning/schnetpack) and [SHARC](https://sharc-md.org/) | [Westermayr et al., *J. Phys. Chem. Lett.* **11** (2020) 3828](https://doi.org/10.1021/acs.jpclett.0c00527) | 41 | | [PANNA](https://gitlab.com/PANNAdevs/panna) | Properties from neural network architectures | [Lot et al., *Comput. Phys. Commun.* **256**, (2020) 107402](https://doi.org/10.1016/j.cpc.2020.107402) | 42 | | [TorchANI](https://github.com/aiqm/torchani) | Pytorch implementation of ANI | [Gao et al., *J. Chem. Inf. Model.*, 10.1021/acs.jcim.0c00451 (2020)](https://doi.org/10.1021/acs.jcim.0c00451) | 43 | 44 | ### Other ML based potential implementations 45 | 46 | | Name | Description | Reference | 47 | | --------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 48 | | [GAP/SOAP](http://libatoms.org/Home/Software) | GPR based ML potential | [Bartók et al., *Phys. Rev. Lett.* **104** (2010) 136403](https://doi.org/10.1103/PhysRevLett.104.136403) [*Phys. Rev. B* **87** (2013) 184115](https://doi.org/10.1103/PhysRevB.87.184115) | 49 | | [SNAP](https://github.com/FitSNAP) | Linear ML potential based on bispectrum components of the local neighbor density | [Thompson et al., *J. Comput. Phys.* **285** (2015) 316](https://doi.org/10.1016/j.jcp.2014.12.018) | 50 | | [AutoForce](https://github.com/amirhajibabaei/AutoForce) | SGPR based ML potential (on-the-fly) | [Hajibabaei et al., *Phys. Rev. B.* **103** (2021) 214102](https://doi.org/10.1103/PhysRevB.103.214102) | 51 | 52 | ## ML tools and packages for materials science and drug discovery applications 53 | 54 | | Name | Description | Reference | 55 | | ---------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | 56 | | [COMBO](https://github.com/tsudalab/combo) | Bayesian Optimization Library | [Ueno et al., *Materials Discovery* **4** (2016) 18](https://doi.org/10.1016/j.md.2016.04.001) | 57 | | [Magpie](https://bitbucket.org/wolverton/magpie) | ML framework | [Ward, Wolverton et al., *npj Computational Materials.* **2** (2016) 16028](https://doi.org/10.1038/npjcompumats.2016.28) | 58 | | [PROPhet](https://biklooost.github.io/PROPhet) | Neural networks to materials predictions | [Kolb et al., *Sci Rep* **7** (2017) 1192](https://www.nature.com/articles/s41598-017-01251-z) | 59 | | [SISSO](https://github.com/rouyang2017/SISSO) | ML framework | [Ouyang, Ghiringhelli et al., *Phys. Rev. Mater.* **2**, (2018) 083802](https://doi.org/10.1103/PhysRevMaterials.2.083802) | 60 | | [MatMiner](https://hackingmaterials.github.io/matminer) | Feature construction library | [Ward, Jian et al., *Comput. Mater. Sci.* **152** (2018) 60](https://doi.org/10.1016/j.commatsci.2018.05.018) | 61 | | [AFLOW-ML](http://aflowlib.org/aflow-ml) | ML framework | [Gossett, Curtarolo et al., *Comput. Mater. Sci.* **152** (2018) 134](https://doi.org/10.1016/j.commatsci.2018.03.075) | 62 | | [Phoenics](https://github.com/aspuru-guzik-group/phoenics) | Bayesian Optimization and kernel density estimation | [Häse et al., *ACS Cent. Sci.* **4** (2018) 1134](https://pubs.acs.org/doi/abs/10.1021/acscentsci.8b00307) | 63 | | [JARVIS-ML](https://ctcms.nist.gov/jarvisml) | Properties predictions | [Choudhary et al., *Phys. Rev. Materials* **2** (2018) 083801](https://doi.org/10.1103/PhysRevMaterials.2.083801) | 64 | | [OMDB-ML](https://omdb.mathub.io/ml) | Properties predictions | [Olsthoorn et al., *Adv. Quantum Technol.* **2** (2019) 1900023](https://doi.org/10.1002/qute.201900023) | 65 | | [DeepChem](https://deepchem.io) | Democratizing Deep-Learning for Drug Discovery | [Ramsundar et al., *O'Reilly Media* (2019)](https://www.oreilly.com/library/view/deep-learning-for/9781492039822) | 66 | | [ShiftML](http://shiftml.org) | ML framework for predicting chemical shifts in molecular solids | [Paruzzo et al., *Nat. Commun.* **9** (2019) 4501](https://doi.org/10.1038/s41467-018-06972-x) | 67 | | [MaterialNet](https://github.com/ToyotaResearchInstitute/materialnet) | A web-based graph explorer for materials science data | [Choudhury et al., *JOSS* **5**, (2020) 2105](https://doi.org/10.21105/joss.02105) 68 | 69 | ## Databases 70 | 71 | ### General databases 72 | 73 | | Name | Description | Reference | 74 | | ---------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | 75 | | [NOMAD Repository](https://nomad-repository.eu) | Open-Access Platform for Data Sharing | [Draxl, Scheffler, *J. Phys. Mater.* **2** (2019) 036001](https://doi.org/10.1088/2515-7639/ab13bb) | 76 | | [Materials Cloud](http://www.materialscloud.org) | Platform for Open Computational Science | [Talirz et al., arXiv:2003.12510 (2020)](https://arxiv.org/abs/2003.12510) | 77 | 78 | ### Databases for inorganic materials 79 | 80 | | Name | Description | Reference | 81 | | ---------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | 82 | | [American Mineralogist Crystal Structure Database](http://rruff.geo.arizona.edu/AMS/amcsd.php) |Crystal structure database for mineralogist | [Downs and Hall-Wallace, *American Mineralogist* **88** (2003) 247](https://www.geo.arizona.edu/xtal/group/pdf/am88_247.pdf) | 83 | | [COD](http://www.crystallography.net/cod/search.html) | Crystallography Open Database | [Grazulis et al. (2009)](http://scripts.iucr.org/cgi-bin/paper?S0021889809016690), [Gražulis (2012)](https://doi.org/10.1093/nar/gkr900), [Gražulis (2015)](http://scripts.iucr.org/cgi-bin/paper?S1600576714025904), [Merkys (2016)](http://scripts.iucr.org/cgi-bin/paper?S1600576715022396), [Quirós (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0279-6), [Vaitkus (2021)](https://scripts.iucr.org/cgi-bin/paper?S1600576720016532)| 84 | | [AFLOW](http://www.aflowlib.org) | Ab initio computational materials science database | [Curtarolo et al., *Cumput. Mater. Sci.* **58** (2012) 218](https://doi.org/10.1016/j.commatsci.2012.02.005) | 85 | | [NREL MatDB](https://materials.nrel.gov) | Computational materials database with focus on renewable energy applications | [Stevanovic et al. (2012)](http://dx.doi.org/10.1103/PhysRevB.85.115104), [Lany (2013)](http://dx.doi.org/10.1103/PhysRevB.87.085112), [Lany (2015)](http://dx.doi.org/10.1088/0953-8984/27/28/283203) | 86 | | [Materials Project](https://materialsproject.org) | A materials genome approach to accelerating materials innovation | [Jain et al., *APL Materials* **1** (2013) 011002](https://doi.org/10.1063/1.4812323) 87 | | [OQMD](http://oqmd.org) | Database of DFT calculated thermodynamic and structural materials properties | [Kirklin et al., *Npj Comput. Mater.* **1** (2015) 15010](http://dx.doi.org/10.1038/npjcompumats.2015.10) | 88 | | [COMBO](https://github.com/tsudalab/combo) | Bayesian Optimization Library | [Ueno et al., *Materials Discovery* **4** (2016) 18](https://doi.org/10.1016/j.md.2016.04.001) | 89 | | [Open Catalyst Project](https://github.com/Open-Catalyst-Project/ocp/blob/master/DATASET.md) | Using AI to model and discover new catalysts to address the energy challenges posed by climate change     | [Facebook AI and Carnegie Mellon (2020)](https://opencatalystproject.org) | 90 | | [JARVIS-API](https://jarvis.nist.gov) | Integrated Infrastructure for Data-driven Materials Design | [Choudhary et al., arXiv:2007.01831 (2020)](https://arxiv.org/abs/2007.01831) | 91 | 92 | ### Databases for organic molecules and materials 93 | 94 | | Name | Description | Reference | 95 | | ---------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | 96 | | [MoleculeNet](http://moleculenet.ai) | Large scale benchmark for molecular machine learning | [Wu et al., *Chem. Sci.* **9** (2018) 513](https://doi.org/10.1039/C7SC02664A) | 97 | | [FMODB](https://drugdesign.riken.jp/FMODB/) | Database of quantum mechanical FMO calculations | [Kato et al., *J. Chem. Inf. Model.* 10.1021/acs.jcim.0c00273 (2020)](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00273) | 98 | | [QM-sym](https://github.com/XI-Lab/QM-sym-database) | Symmetrized quantum chemistry database of 135k organic molecules | [Liang et al., *Sci. Data* **6** (2020) 213](https://doi.org/10.1038/s41597-019-0237-9) | 99 | 100 | ## Workflow management 101 | 102 | | Name | Description | Reference | 103 | | ---------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | 104 | | [Research Object Crate](https://www.researchobject.org/) | A JSON-based approach for research object serialization | [Bechhofer et al., *Future Generation Computer Systems* **29** (2013) 599-611](https://doi.org/10.1016/j.future.2011.08.004) | 105 | | [Common Workflow Language](https://www.commonwl.org/) | An open standard for analysis workflows and tools | [Amstutz et al., *Common Workflow Language, v1.0* (2016)](https://doi.org/10.6084/m9.figshare.3115156.v2) | 106 | | [DLHub](https://www.dlhub.org/) | Sharing of ML models and workflows | [Chard et al., *IEEE IPDPS* (2019) 283-292](https://arxiv.org/pdf/1811.11213), [Blaiszik et al., *MRS Commun.* **9** (2019) 1125–1133](https://doi.org/10.1557/mrc.2019.118) | 107 | 108 | 109 | ## Peer-reviewed articles referring to this document 110 | 111 | 1. H. Guo, Q. Wang, A Stuke, A. Urban, and N. Artrith, *Front. Energy Res.* just accepted, **(2021)** **Open Access**. 112 | 2. A. M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith, 113 | *Machine Learning: Science and Technology*, in press, **(2021)** **Open Access** DOI: https://doi.org/10.1088/2632-2153/abfd96 . 114 | 3. T. Morawietz and N. Artrith, 115 | *J. Comput. Aided Mol. Des.* **35**, 557-586 (2021) **Open Access** DOI: https://doi.org/10.1007/s10822-020-00346-6 . 116 | 117 | --------------------------------------------------------------------------------