mumerchem / tools-and-data

A collection of tools and databases for atomistic machine learning

Contributors: 2021 N. Artrith ([email protected]), T. Morawietz, H. Guo, and A. Urban

List of Public Tools and Data for Atomistic Machine Learning

A Collection of Public Open-Source Tools and Databases for Atomistic Machine Learning

Table of Contents

• Contributing
• ML atomistic potentials
  • ANN based potential implementations
  • Other ML based potential implementations
• ML tools and packages for materials science and drug discovery applications
• Databases
  • General databases
  • Databases for inorganic materials
  • Databases for organic molecules and materials
• Workflow management
• Peer-reviewed articles referring to this document

Contributing

We welcome everybody to contribute to this list. Your name will be added to the list of contributors at the top of this document.

ML atomistic potentials

ANN based potential implementations

Entries sorted by the year of the publication.

Name	Features	Reference
ænet	Capable of handling many chemical species	Artrith, Urban, Comput. Mater. Sci. 114 (2016) 135
Amp	Large descriptor library	Khorshidi, Peterson, Comput. Phys. Commun. 207 (2016) 310
ANI	Accurate potential for molecular systems	Smith, Isayev, Roitberg, Chem. Sci. 8 (2017) 3192
TensorMol	Electrostatics and van der Waals interactions	Yao et al., Chem. Sci. 9 (2018) 2261
DeePMD-kit	GPU support	Wang et al., Comput. Phys. Commun. 228 (2018) 178
SchNetPack	Feature learning	Schütt et al., J. Chem. Theory Comput. 15 (2019) 448
N2P2	Behler-Parinello neural network potential	Singraber et al., J. Chem. Theory Comput. 15 (2019) 1827
SchNarc	Extension to multiple electronic states based on SchNet and SHARC	Westermayr et al., J. Phys. Chem. Lett. 11 (2020) 3828
PANNA	Properties from neural network architectures	Lot et al., Comput. Phys. Commun. 256, (2020) 107402
TorchANI	Pytorch implementation of ANI	Gao et al., J. Chem. Inf. Model., 10.1021/acs.jcim.0c00451 (2020)

Other ML based potential implementations

Name	Description	Reference
GAP/SOAP	GPR based ML potential	Bartók et al., Phys. Rev. Lett. 104 (2010) 136403 Phys. Rev. B 87 (2013) 184115
SNAP	Linear ML potential based on bispectrum components of the local neighbor density	Thompson et al., J. Comput. Phys. 285 (2015) 316
AutoForce	SGPR based ML potential (on-the-fly)	Hajibabaei et al., Phys. Rev. B. 103 (2021) 214102

ML tools and packages for materials science and drug discovery applications

Name	Description	Reference
COMBO	Bayesian Optimization Library	Ueno et al., Materials Discovery 4 (2016) 18
Magpie	ML framework	Ward, Wolverton et al., npj Computational Materials. 2 (2016) 16028
PROPhet	Neural networks to materials predictions	Kolb et al., Sci Rep 7 (2017) 1192
SISSO	ML framework	Ouyang, Ghiringhelli et al., Phys. Rev. Mater. 2, (2018) 083802
MatMiner	Feature construction library	Ward, Jian et al., Comput. Mater. Sci. 152 (2018) 60
AFLOW-ML	ML framework	Gossett, Curtarolo et al., Comput. Mater. Sci. 152 (2018) 134
Phoenics	Bayesian Optimization and kernel density estimation	Häse et al., ACS Cent. Sci. 4 (2018) 1134
JARVIS-ML	Properties predictions	Choudhary et al., Phys. Rev. Materials 2 (2018) 083801
OMDB-ML	Properties predictions	Olsthoorn et al., Adv. Quantum Technol. 2 (2019) 1900023
DeepChem	Democratizing Deep-Learning for Drug Discovery	Ramsundar et al., O'Reilly Media (2019)
ShiftML	ML framework for predicting chemical shifts in molecular solids	Paruzzo et al., Nat. Commun. 9 (2019) 4501
MaterialNet	A web-based graph explorer for materials science data	Choudhury et al., JOSS 5, (2020) 2105

Databases

General databases

Name	Description	Reference
NOMAD Repository	Open-Access Platform for Data Sharing	Draxl, Scheffler, J. Phys. Mater. 2 (2019) 036001
Materials Cloud	Platform for Open Computational Science	Talirz et al., arXiv:2003.12510 (2020)

Databases for inorganic materials

Name	Description	Reference
American Mineralogist Crystal Structure Database	Crystal structure database for mineralogist	Downs and Hall-Wallace, American Mineralogist 88 (2003) 247
COD	Crystallography Open Database	Grazulis et al. (2009), Gražulis (2012), Gražulis (2015), Merkys (2016), Quirós (2018), Vaitkus (2021)
AFLOW	Ab initio computational materials science database	Curtarolo et al., Cumput. Mater. Sci. 58 (2012) 218
NREL MatDB	Computational materials database with focus on renewable energy applications	Stevanovic et al. (2012), Lany (2013), Lany (2015)
Materials Project	A materials genome approach to accelerating materials innovation	Jain et al., APL Materials 1 (2013) 011002
OQMD	Database of DFT calculated thermodynamic and structural materials properties	Kirklin et al., Npj Comput. Mater. 1 (2015) 15010
COMBO	Bayesian Optimization Library	Ueno et al., Materials Discovery 4 (2016) 18
Open Catalyst Project	Using AI to model and discover new catalysts to address the energy challenges posed by climate change	Facebook AI and Carnegie Mellon (2020)
JARVIS-API	Integrated Infrastructure for Data-driven Materials Design	Choudhary et al., arXiv:2007.01831 (2020)

Databases for organic molecules and materials

Name	Description	Reference
MoleculeNet	Large scale benchmark for molecular machine learning	Wu et al., Chem. Sci. 9 (2018) 513
FMODB	Database of quantum mechanical FMO calculations	Kato et al., J. Chem. Inf. Model. 10.1021/acs.jcim.0c00273 (2020)
QM-sym	Symmetrized quantum chemistry database of 135k organic molecules	Liang et al., Sci. Data 6 (2020) 213

Workflow management

Name	Description	Reference
Research Object Crate	A JSON-based approach for research object serialization	Bechhofer et al., Future Generation Computer Systems 29 (2013) 599-611
Common Workflow Language	An open standard for analysis workflows and tools	Amstutz et al., Common Workflow Language, v1.0 (2016)
DLHub	Sharing of ML models and workflows	Chard et al., IEEE IPDPS (2019) 283-292, Blaiszik et al., MRS Commun. 9 (2019) 1125–1133

Peer-reviewed articles referring to this document

1. H. Guo, Q. Wang, A Stuke, A. Urban, and N. Artrith, Front. Energy Res. just accepted, (2021) Open Access.
2. A. M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith, Machine Learning: Science and Technology, in press, (2021) Open Access DOI: https://doi.org/10.1088/2632-2153/abfd96 .
3. T. Morawietz and N. Artrith, J. Comput. Aided Mol. Des. 35, 557-586 (2021) Open Access DOI: https://doi.org/10.1007/s10822-020-00346-6 .