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
We welcome everybody to contribute to this list. Your name will be added to the list of contributors at the top of this document.
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
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 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
Peer-reviewed articles referring to this document
H. Guo, Q. Wang, A Stuke, A. Urban, and N. Artrith, Front. Energy Res. just accepted, (2021) Open Access .
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 .
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 .