Tkatchenko, Alexandre ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
External co-authors :
yes
Language :
English
Title :
Roadmap on Machine learning in electronic structure
Publication date :
19 August 2022
Journal title :
IOP Conference Series: Materials Science and Engineering
Janet J P Liu F Nandy A Duan C Yang T Lin S Kulik H J 2019 Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry Inorg. Chem. 58 10592 10606 10592-606 10.1021/acs.inorgchem.9b00109
Janet J P Kulik H J 2017 Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships J. Phys. Chem. A 121 8939 8954 8939-54 10.1021/acs.jpca.7b08750
Meyer B Sawatlon B Heinen S Anatole von Lilienfeld O Corminboeuf C 2018 Machine learning meets volcano plots: computational discovery of cross-coupling catalysts Chem. Sci. 9 7069 7077 7069-77 10.1039/c8sc01949e
Janet J P Chan L Kulik H J 2018 Accelerating chemical discovery with machine learning: simulated evolution of spin crossover complexes with an artificial neural network J. Phys. Chem. Lett. 9 1064 1071 1064-71 10.1021/acs.jpclett.8b00170
Janet J P Ramesh S Duan C Kulik H J 2020 Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization ACS Cent. Sci. 6 513 524 513-24 10.1021/acscentsci.0c00026
Herbol H C Poloczek M Clancy P 2020 Cost-effective materials discovery: Bayesian optimization across multiple information sources Mater. Horiz. 7 2113 2123 2113-23 10.1039/d0mh00062k
Janet J P Duan C Yang T Nandy A Kulik H J 2019 A quantitative uncertainty metric controls error in neural network-driven chemical discovery Chem. Sci. 10 7913 7922 7913-22 10.1039/c9sc02298h
Liu F Duan C Kulik H J 2020 Rapid detection of strong correlation with machine learning for transition-metal complex high-throughput screening J. Phys. Chem. Lett. 11 8067 8076 8067-76 10.1021/acs.jpclett.0c02288
Duan C Janet J P Liu F Nandy A Kulik H J 2019 Learning from failure: predicting electronic structure calculation outcomes with machine learning models J. Chem. Theory Comput. 15 2331 2345 2331-45 10.1021/acs.jctc.9b00057
Stein C J Reiher M 2016 Automated selection of active orbital spaces J. Chem. Theory Comput. 12 1760 1771 1760-71 10.1021/acs.jctc.6b00156
Pettifor D G 1984 A chemical scale for crystal-structure maps Solid State Commun. 51 31 10.1016/0038-1098(84)90765-8
Glawe H Sanna A Gross E K U Marques M A L 2016 The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining New J. Phys. 18 093011 10.1088/1367-2630/18/9/093011
Bialon A F Hammerschmidt T Drautz R 2016 Three-parameter crystal-structure prediction for sp-d valent compounds Chem. Mater. 28 2550 10.1021/acs.chemmater.5b04299
Jenke J Subramanyam A P A Densow M Hammerschmidt T Pettifor D G Drautz R 2018 Electronic structure based descriptor for characterizing local atomic environments Phys. Rev. B 98 144102 10.1103/physrevb.98.144102
Sutton C 2019 Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition npj Comput. Mater. 5 111 10.1038/s41524-019-0239-3
Rosenbrock C W Homer E R Csányi G Hart G L W 2017 Discovering the building blocks of atomic systems using machine learning: application to grain boundaries npj Comput. Mater. 3 29 10.1038/s41524-017-0027-x
Reimann D Nidadavolu K ul Hassan H Vajragupta N Glasmachers T Junker P Hartmaier A 2019 Modeling macroscopic material behavior with machine learning algorithms trained by micromechanical simulations Front. Mater. 6 181 10.3389/fmats.2019.00181
Volz N 2021 Understanding creep of a single-crystalline Co-Al-W-Ta superalloy by studying the deformation mechanism, segregation tendency and stacking fault energy Acta Mater. 214 117019 10.1016/j.actamat.2021.117019
Huang B Anatole von Lilienfeld O 2020 Quantum machine learning using atom-in-molecule-based fragments selected on the fly Nat. Chem. 12 945 10.1038/s41557-020-0527-z
Jenke J Ladines A Hammerschmidt T Pettifor D G Drautz R 2021 Tight-binding bond parameters for dimers across the periodic table from density-functional theory Phys. Rev. Mater. 5 023801 10.1103/physrevmaterials.5.023801
Woodley S M Catlow R 2008 Crystal structure prediction from first principles Nat. Mater. 7 937 946 937-46 10.1038/nmat2321
Faber F A Lindmaa A Anatole von Lilienfeld O Armiento R 2016 Machine learning energies of 2 million elpasolite (ABC2D6) crystals Phys. Rev. Lett. 117 135502 10.1103/physrevlett.117.135502
Schmidt J Shi J Borlido P Chen L Botti S Marques M A L 2017 Predicting the thermodynamic stability of solids combining density functional theory and machine learning Chem. Mater. 29 5090 5103 5090-103 10.1021/acs.chemmater.7b00156
Faber F Lindmaa A Anatole von Lilienfeld O Armiento R 2015 Crystal structure representations for machine learning models of formation energies Int. J. Quantum Chem. 115 1094 1101 1094-101 10.1002/qua.24917
Ward L Liu R Krishna A Hegde V I Agrawal A Choudhary A Wolverton C 2017 Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations Phys. Rev. B 96 024104 10.1103/physrevb.96.024104
Xie T Grossman J C 2018 Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties Phys. Rev. Lett. 120 145301 10.1103/physrevlett.120.145301
Goodall R E A Lee A A 2019 Predicting materials properties without crystal structure: deep representation learning from stoichiometry (arXiv: 1910.00617)
Bartel C J Trewartha A Wang Q Dunn A Jain A Ceder G 2020 A critical examination of compound stability predictions from machine-learned formation energies (arXiv: 2001.10591)
Kim K Ward L He J Krishna A Agrawal A Wolverton C 2018 Machine-learning-accelerated high-throughput materials screening: discovery of novel quaternary Heusler compounds Phys. Rev. Mater. 2 123801 10.1103/physrevmaterials.2.123801
Park C W Wolverton C 2020 Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery Phys. Rev. Mater. 4 063801 10.1103/physrevmaterials.4.063801
Jha D Ward L Paul A Liao W-k Choudhary A Wolverton C Agrawal A 2018 ElemNet: deep learning the chemistry of materials from only elemental composition Sci. Rep. 8 17593 10.1038/s41598-018-35934-y
Gilmer J Schoenholz S S Riley P F Vinyals O Dahl G E 2017 Neural message passing for quantum chemistry Proc. 34th Int. Conf. Machine Learning vol 70 1263 1272 1263-72 JMLR.org
LeCun Y Bengio Y Hinton G 2015 Deep learning Nature 521 436 444 436-44 10.1038/nature14539
Perdew J P Burke K Ernzerhof M 1996 Generalized gradient approximation made simple Phys. Rev. Lett. 77 3865 3868 3865-8 10.1103/physrevlett.77.3865
Sun J Ruzsinszky A Perdew J P 2015 Strongly constrained and appropriately normed semilocal density functional Phys. Rev. Lett. 115 036402 10.1103/physrevlett.115.036402
Jain A 2013 Commentary: the materials project: a materials genome approach to accelerating materials innovation APL Mater. 1 011002 10.1063/1.4812323
Saal J E Kirklin S Aykol M Meredig B Wolverton C 2013 Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD) JOM 65 1501 1509 1501-9 10.1007/s11837-013-0755-4
Curtarolo S 2012 AFLOW: an automatic framework for high-throughput materials discovery Comput. Mater. Sci. 58 218 226 218-26 10.1016/j.commatsci.2012.02.005
Wang H-C Botti S Marques M A L Marques L 2021 Predicting stable crystalline compounds using chemical similarity npj Comput. Mater. 7 12 10.1038/s41524-020-00481-6
Sutton R 2018 The bitter lesson http://incompleteideas.net/IncIdeas/BitterLesson.html
Draxl C Scheffler M 2020 Big data-driven materials science and its FAIR data infrastructure Handbook of Materials Modeling Yip S Andreoni W Berlin Springer p 49
Foppa L 2021 Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence MRS Bull. 46 1016 1026 1016-26 10.1557/s43577-021-00165-6
Ghiringhelli L M Vybiral J Levchenko S V Draxl C Scheffler M 2015 Big data of materials science: critical role of the descriptor Phys. Rev. Lett. 114 105503 10.1103/physrevlett.114.105503
Ouyang R Curtarolo S Ahmetcik E Scheffler M Ghiringhelli L M 2018 SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates Phys. Rev. Mater. 2 083802 10.1103/physrevmaterials.2.083802
Goldsmith B R Boley M Vreeken J Scheffler M Ghiringhelli L M 2017 Uncovering structure-property relationships of materials by subgroup discovery New J. Phys. 19 013031 10.1088/1367-2630/aa57c2
Frazier P I Wang J 2016 Bayesian optimization for materials design Information Science for Materials Discovery and Design Lookman T Alexander F J Rajan K Berlin Springer 45 75 pp 45-75
Ghiringhelli L M 2021 An AI-toolkit to develop and share research into new materials Nat. Rev. Phys. 3 724 10.1038/s42254-021-00373-8
Scott J G Berger J O 2006 An exploration of aspects of Bayesian multiple testing J. Stat. Plan. Inference 136 2144 2162 2144-62 10.1016/j.jspi.2005.08.031
Boley M Goldsmith B R Ghiringhelli L M Vreeken J 2017 Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery Data Min. Knowl. Discov. 31 1391 1418 1391-418 10.1007/s10618-017-0520-3
Ghosh K Stuke A Todorović M Jørgensen P B Schmidt M N Vehtari A Rinke P 2019 Deep learning spectroscopy: neural networks for molecular excitation spectra Adv. Sci. 6 1801367 10.1002/advs.201801367
Chandrasekaran A Kamal D Batra R Kim C Chen L Ramprasad R 2019 Solving the electronic structure problem with machine learning npj Comput. Mater. 5 22 10.1038/s41524-019-0162-7
Westermayr J Marquetand P 2021 Machine learning for electronically excited states of molecules Chem. Rev. 121 9873 10.1021/acs.chemrev.0c00749
Bağcıoğlu M Fricker M Johler S Ehling-Schulz M 2019 Detection and identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via machine learning based FTIR spectroscopy Front. Microbiol. 10 902 10.3389/fmicb.2019.00902
Rehman I U Khan R S Rehman S 2020 Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics Expert Rev. Mol. Diagn. 20 749 755 749-55 10.1080/14737159.2020.1784008
Toyao T Maeno Z Takakusagi S Kamachi T Takigawa I Shimizu K-i 2020 Machine learning for catalysis informatics: recent applications and prospects ACS Catal. 10 2260 2297 2260-97 10.1021/acscatal.9b04186
Timoshenko J Lu D Lin Y Frenkel A I 2017 Supervised machine-learning-based determination of three-dimensional structure of metallic nanoparticles J. Phys. Chem. Lett. 8 5091 10.1021/acs.jpclett.7b02364
Cordova M Balodis M Simões de Almeida B Ceriotti M Emsley L 2021 Bayesian probabilistic assignment of chemical shifts in organic solids Sci. Adv. 7 eabk2341 10.1126/sciadv.abk2341
Ren H Li H Zhang Q Liang L Guo W Huang F Luo Y Jiang J 2021 A machine learning vibrational spectroscopy protocol for spectrum prediction and spectrum-based structure recognition Fundam. Res. 1 488 10.1016/j.fmre.2021.05.005
Jinadasa M H W N Kahawalage A C Halstensen M Skeie N O Jens K J 2021 Deep learning approach for Raman spectroscopy Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization London IntechOpen
Zhao Y Zhan K Wang Z Shen W 2021 Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line Prog. Photovolt., Res. Appl. 29 471 484 471-84 10.1002/pip.3395
Zhaochun Z Ruiwu P Nianyi C 1998 Artificial neural network prediction of the band gap and melting point of binary and ternary compound semiconductors Mater. Sci. Eng. B 54 149 152 149-52 10.1016/s0921-5107(98)00157-3
Paul S Johann G Henrik T Reiner S 2000 Rapid access to infrared reference spectra of arbitrary organic compounds: scope and limitations of an approach to the simulation of infrared spectra by neural networks Chem. Eur. J. 6 920 927 920-7 10.1002/(sici)1521-3765(20000303)6:5<920::aid-chem920>3.0.co;2-w
Himanen L Geurts A Foster A S Rinke P 2019 Data-driven materials science: status, challenges, and perspectives Adv. Sci. 6 1900808 10.1002/advs.201900808
Stuke A Kunkel C Golze D Todorović M Margraf J T Reuter K Rinke P Oberhofer H 2020 Atomic structures and orbital energies of 61 489 crystal-forming organic molecules Sci. Data 7 58 10.1038/s41597-020-0385-y
Xian R P 2020 An open-source, end-to-end workflow for multidimensional photoemission spectroscopy Sci. Data 7 442 10.1038/s41597-020-00769-8
Pilania G Gubernatis J E Lookman T 2017 Multi-fidelity machine learning models for accurate bandgap predictions of solids Comput. Mater. Sci. 129 156 10.1016/j.commatsci.2016.12.004
Perim E 2016 Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases Nat. Commun. 7 12315 10.1038/ncomms12315
Ford D C Hicks D Oses C Toher C Curtarolo S 2019 Metallic glasses for biodegradable implants Acta Mater. 176 297 305 297-305 10.1016/j.actamat.2019.07.008
Ren F Ward L Williams T Laws K J Wolverton C Hattrick-Simpers J Mehta A 2018 Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments Sci. Adv. 4 eaaq1566 10.1126/sciadv.aaq1566
Kusne A G 2020 On-the-fly closed-loop materials discovery via Bayesian active learning Nat. Commun. 11 5966 10.1038/s41467-020-19597-w
Oses C Toher C Curtarolo S 2020 High-entropy ceramics Nat. Rev. Mater. 5 295 309 295-309 10.1038/s41578-019-0170-8
Dasgupta A Broderick S R Mack C Kota B U Subramanian R Setlur S Govindaraju V Rajan K 2019 Probabilistic assessment of glass forming ability rules for metallic glasses aided by automated analysis of phase diagrams Sci. Rep. 9 357 10.1038/s41598-018-36224-3
Sarker P Harrington T Toher C Oses C Samiee M Maria J-P Brenner D W Vecchio K S Curtarolo S 2018 High-entropy high-hardness metal carbides discovered by entropy descriptors Nat. Commun. 9 4980 10.1038/s41467-018-07160-7
Lederer Y Toher C Vecchio K S Curtarolo S 2018 The search for high entropy alloys: a high-throughput ab initio approach Acta Mater. 159 364 383 364-83 10.1016/j.actamat.2018.07.042
Rickman J M Chan H M Harmer M P Smeltzer J A Marvel C J Roy A Balasubramanian G 2019 Materials informatics for the screening of multi-principal elements and high-entropy alloys Nat. Commun. 10 2618 10.1038/s41467-019-10533-1
Grabowski B Ikeda Y Srinivasan P Körmann F Freysoldt C Duff A I Shapeev A Neugebauer J 2019 Ab initio vibrational free energies including anharmonicity for multicomponent alloys npj Comput. Mater. 5 80 10.1038/s41524-019-0218-8
Noé F Tkatchenko A Müller K-R Clementi C 2020 Machine learning for molecular simulation Ann. Rev. Phys. Chem. 71 361 10.1146/annurev-physchem-042018-052331
Anatole von Lilienfeld O Müller K-R Tkatchenko A 2020 Exploring chemical compound space with quantum-based machine learning Nat. Rev. Chem. 4 347 10.1038/s41570-020-0189-9
Deringer V L Bernstein N Bartók A P Cliffe M J Kerber R N Marbella L E Grey C P Elliott S R Csányi G 2018 Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics J. Phys. Chem. Lett. 9 2879 10.1021/acs.jpclett.8b00902
Chmiela S Sauceda H E Müller K-R Tkatchenko A 2018 Towards exact molecular dynamics simulations with machine-learned force fields Nat. Commun. 9 3887 10.1038/s41467-018-06169-2
Schütt K T Arbabzadah F Chmiela S Müller K-R Tkatchenko A 2017 Quantum-chemical insights from deep tensor neural networks Nat. Commun. 8 13890 10.1038/ncomms13890
Zubatyuk R Smith J S Leszczynski J Isayev O 2019 Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network Sci. Adv. 5 eaav6490 10.1126/sciadv.aav6490
Schütt K T Gastegger M Tkatchenko A Müller K-R Maurer R J 2019 Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Nat. Commun. 10 5024 10.1038/s41467-019-12875-2
Segler M H S Preuss M Waller M P 2018 Planning chemical syntheses with deep neural networks and symbolic AI Nature 555 604 10.1038/nature25978
Wilkins D M Grisafi A Yang Y Lao K U DiStasio R A Jr Ceriotti M 2019 Accurate molecular polarizabilities with coupled cluster theory and machine learning Proc. Natl Acad. Sci. USA 116 3401 10.1073/pnas.1816132116
Ercolessi F Adams J B 1994 Interatomic potentials from first-principles calculations: the force-matching method Europhys. Lett. 26 583 10.1209/0295-5075/26/8/005
Senftle T P 2016 The ReaxFF reactive force-field: development, applications and future directions npj Comput. Mater. 2 15011 10.1038/npjcompumats.2015.11
Mackay D J C 2003 Information Theory, Inference, and Learning Algorithms Cambridge Cambridge University Press
Bartók A P Payne M C Kondor R Csányi G 2010 Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons Phys. Rev. Lett. 104 136403 10.1103/PhysRevLett.104.136403
Bartók A P Kondor R Csányi G 2013 On representing chemical environments Phys. Rev. B 87 184115 10.1103/PhysRevB.87.184115
Bartók A P Kermode J Bernstein N Csányi G 2018 Machine learning a general-purpose interatomic potential for silicon Phys. Rev. X 8 041048 10.1103/PhysRevX.8.041048
Vandermause J Torrisi S B Batzner S Xie Y Sun L Kolpak A M Kozinsky B 2020 On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events npj Comput. Mater. 6 20 10.1038/s41524-020-0283-z
Bauer M van der Wilk M Rasmussen C E 2016 Understanding probabilistic sparse Gaussian process approximations Advances in Neural Information Processing Systems
Deringer V L Caro M A Csányi G 2020 A general-purpose machine-learning force field for bulk and nanostructured phosphorus Nat. Commun. 11 5461 10.1038/s41467-020-19168-z
Caro M A Deringer V L Koskinen J Laurila T Csányi G 2018 Growth mechanism and origin of high sp 3 content in tetrahedral amorphous carbon Phys. Rev. Lett. 120 166101 10.1103/physrevlett.120.166101
Manzhos S Carrington T 2020 Neural network potential energy surfaces for small molecules and reactions Chem. Rev. 121 10187 10217 10187-217 10.1021/acs.chemrev.0c00665
Beck M Jäckle A Worth G A Meyer H D 2000 The multiconfiguration time-dependent Hartree (MCTDH) method: a highly efficient algorithm for propagating wavepackets Phys. Rep. 324 1 105 1-105 10.1016/s0370-1573(99)00047-2
Leclerc A Carrington T 2014 Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices J. Chem. Phys. 140 174111 10.1063/1.4871981
Manzhos S Carrington T 2006 Using neural networks to represent potential surfaces as sums of products J. Chem. Phys. 125 194105 10.1063/1.2387950
Pradhan E Brown A 2016 Vibrational energies for HFCO using a neural network sum of exponentials potential energy surface J. Chem. Phys. 144 174305 10.1063/1.4948440
Majumder M Hegger S E Dawes R Manzhos S Wang X-G Tucker C Li J Guo H 2015 Explicitly correlated MRCI-F12 potential energy surfaces for methane fit with several permutation invariant schemes and full-dimensional vibrational calculations Mol. Phys. 113 1823 1833 1823-33 10.1080/00268976.2015.1015642
Castro E Avila G Manzhos S Agarwal J Schaefer H F III Carrington T 2017 Applying a Smolyak collocation method to Cl2CO Mol. Phys. 115 1775 1785 1775-85 10.1080/00268976.2016.1271153
Kamath A Vargas-Hernández R A Krems R V Carrington T Manzhos S 2018 Neural networks vs Gaussian Process regression for representing potential energy surfaces: a comparative study of fit quality and vibrational spectrum accuracy J. Chem. Phys. 148 241702 10.1063/1.5003074
Boussaidi M A Ren O Voytsekhovsky D Manzhos S 2020 Random sampling high dimensional model representation Gaussian process regression (RS-HDMR-GPR) for multivariate function representation: application to molecular potential energy surfaces J. Phys. Chem. A 124 7598 7607 7598-607 10.1021/acs.jpca.0c05935
Manzhos S Wang X Carrington T 2018 A multimode-like scheme for selecting the centers of Gaussian basis functions when computing vibrational spectra Chem. Phys. 509 139 144 139-44 10.1016/j.chemphys.2017.10.006
Ku J Kamath A Carrington T Manzhos S 2019 Machine learning optimization of the collocation point set for solving the Kohn-Sham equation J. Phys. Chem. A 123 10631 10642 10631-42 10.1021/acs.jpca.9b09732
Schütt K T Sauceda H E Kindermans P-J Tkatchenko A Müller K-R 2018 SchNet—a deep learning architecture for molecules and materials J. Chem. Phys. 148 241722 10.1063/1.5019779
Manzhos S Chan M Carrington T 2013 Communication: favorable dimensionality scaling of rectangular collocation with adaptable basis functions up to 7 dimensions J. Chem. Phys. 139 051101 10.1063/1.4817182
Blank T B Brown S D Calhoun A W Doren D J 1995 Neural network models of potential energy surfaces J. Chem. Phys. 103 4129 10.1063/1.469597
Behler J Parrinello M 2007 Generalized neural-network representation of high-dimensional potential-energy surfaces Phys. Rev. Lett. 98 146401 10.1103/physrevlett.98.146401
Artrith N Morawietz T Behler J 2011 High-dimensional neural-network potentials for multicomponent systems: applications to zinc oxide Phys. Rev. B 83 153101 10.1103/physrevb.83.153101
Ghasemi S A Hofstetter A Saha S Goedecker S 2015 Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network Phys. Rev. B 92 045131 10.1103/physrevb.92.045131
Ko T W Finkler J A Goedecker S Behler J 2021 A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Nat. Commun. 12 398 10.1038/s41467-020-20427-2
Artrith N Behler J 2012 High-dimensional neural network potentials for metal surfaces: a prototype study for copper Phys. Rev. B 85 045439 10.1103/physrevb.85.045439
Podryabinkin E V Shapeev A V 2017 Active learning of linearly parametrized interatomic potentials Comput. Mater. Sci. 140 171 10.1016/j.commatsci.2017.08.031
Behler J 2011 Atom-centered symmetry functions for constructing high-dimensional neural network potentials J. Chem. Phys. 134 074106 10.1063/1.3553717
Dral P O 2020 Quantum chemistry in the age of machine learning J. Phys. Chem. Lett. 11 2336 2347 2336-47 10.1021/acs.jpclett.9b03664
Smith J S Isayev O Roitberg A E 2017 ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Chem. Sci. 8 3192 3203 3192-203 10.1039/c6sc05720a
Smith J S Nebgen B Lubbers N Isayev O Roitberg A E 2018 Less is more: sampling chemical space with active learning J. Chem. Phys. 148 24 10.1063/1.5023802
Devereux C Smith J S Huddleston K K Barros K Zubatyuk R Isayev O Roitberg A E 2020 Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens J. Chem. Theory Comput. 16 4192 4202 4192-202 10.1021/acs.jctc.0c00121
Schütt K T Sauceda H E Kindermans P J Tkatchenko A Müller K R 2018 SchNet—a deep learning architecture for molecules and materials J. Chem. Phys. 148 241722 10.1063/1.5019779
Lubbers N Smith J S Barros K 2018 Hierarchical modeling of molecular energies using a deep neural network J. Chem. Phys. 148 241715 10.1063/1.5011181
Zubatyuk R Smith J Nebgen B T Tretiak S Isayev O 2021 Teaching a neural network to attach and detach electrons from molecules Nat. Commun. 12 4870 10.1038/s41467-021-24904-0
Zubatiuk T Isayev O 2021 Development of multimodal machine learning potentials: toward a physics-aware artificial intelligence Acc. Chem. Res. 54 1575 1585 1575-85 10.1021/acs.accounts.0c00868
Behler J 2016 Perspective: machine learning potentials for atomistic simulations J. Chem. Phys. 145 170901 10.1063/1.4966192
Musil F Grisafi A Bartók A P Ortner C Csányi G Ceriotti C 2021 Physics-inspired structural representations for molecules and materials chemical reviews Chem. Rev. 121 9759 9815 9759-815 10.1021/acs.chemrev.1c00021
Anderson B Hy T-S Kondor R 2019 Cormorant: covariant molecular neural networks Advances in Neural Information Processing Systems 32 Wallach H Larochelle H Beygelzimer A d'Alché-Buc F Fox E Garnett R 14537 14546 14537-46 Curran Associates, Inc.
Nigam J Pozdnyakov S Ceriotti M 2020 Recursive evaluation and iterative contraction of N-body equivariant features J. Chem. Phys. 153 121101 10.1063/5.0021116
Fabrizio A Grisafi A Meyer B Ceriotti M Corminboeuf C 2019 Electron density learning of non-covalent systems Chem. Sci. 10 9424 9432 9424-32 10.1039/c9sc02696g
Meyer R Weichselbaum M Hauser A W 2020 Machine learning approaches toward orbital-free density functional theory: simultaneous training on the kinetic energy density functional and its functional derivative J. Chem. Theory Comput. 16 5685 5694 5685-94 10.1021/acs.jctc.0c00580
Gastegger M McSloy A Luya M Schütt K T Maurer R J 2020 A deep neural network for molecular wave functions in quasi-atomic minimal basis representation J. Chem. Phys. 153 044123 10.1063/5.0012911
Hermann J Schätzle Z Noé F 2020 Deep-neural-network solution of the electronic Schrödinger equation Nat. Chem. 12 891 897 891-7 10.1038/s41557-020-0544-y
Lopanitsyna N Mahmoud C B Ceriotti M 2021 Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime Phys. Rev. Mater. 5 043802 10.1103/physrevmaterials.5.043802
Bereau T DiStasio R A Jr Tkatchenko A Anatole von Lilienfeld O 2018 Non-covalent interactions across organic and biological subsets of chemical space: physics-based potentials parametrized from machine learning J. Chem. Phys. 148 241706 10.1063/1.5009502
Veit M Wilkins D M Yang Y DiStasio R A Ceriotti M 2020 Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles J. Chem. Phys. 153 024113 10.1063/5.0009106
Grisafi A Nigam J Ceriotti M 2021 Multi-scale approach for the prediction of atomic scale properties Chem. Sci. 12 2078 2090 2078-90 10.1039/d0sc04934d
Westermayr J Gastegger M Schütt K T Maurer R J 2021 Deep integration of machine learning into computational chemistry and materials science (arXiv: 2102.08435)
Welborn M Cheng L Miller T F 2018 Transferability in machine learning for electronic structure via the molecular orbital basis J. Chem. Theory Comput. 14 4772 4779 4772-9 10.1021/acs.jctc.8b00636
Townsend J Vogiatzis K D 2019 Data-driven acceleration of the coupled-cluster singles and doubles iterative solver J. Phys. Chem. Lett. 10 4129 4135 4129-35 10.1021/acs.jpclett.9b01442
Li H Collins C Tanha M Gordon G J Yaron D J 2018 A density functional tight binding layer for deep learning of chemical Hamiltonians J. Chem. Theory Comput. 14 5764 5776 5764-76 10.1021/acs.jctc.8b00873
Wang Z Ye S Wang H He J Huang Q Chang S 2021 Machine learning method for tight-binding Hamiltonian parameterization from ab initio band structure npj Comput. Mater. 7 11 10.1038/s41524-020-00490-5
Stöhr M Medrano Sandonas L Tkatchenko A 2020 Accurate many-body repulsive potentials for density-functional tightbinding from deep tensor neural networks J. Phys. Chem. Lett. 11 6835 6843 6835-43 10.1021/acs.jpclett.0c01307
Westermayr J Marquetand P 2020 Machine learning for electronically excited states Chem. Rev. (accepted) https://doi.org/10.1021/acs.chemrev.0c00749
Thomas L H 1927 The calculation of atomic fields Math. Proc. Camb. Phil. Soc. 23 542 10.1017/s0305004100011683
Kohn W Sham L J 1965 Self-Consistent equations including exchange and correlation effects Phys. Rev. 140 A1133 10.1103/physrev.140.a1133
Perdew J P Ruzsinszky A Jianwei S Nepal N K Kaplan A D 2021 Interpretations of ground-state symmetry breaking and strong correlation in wavefunction and density functional theories Proc. Natl Acad. Sci. USA 118 e2017850118 10.1073/pnas.2017850118
Stoudenmire E M Wagner L O White S R Burke K 2012 One-dimensional continuum electronic structure with the density-matrix renormalization group and its implications for density-functional theory Phys. Rev. Lett. 109 056402 10.1103/physrevlett.109.056402
Tozer D J Ingamells V E Handy N C 1996 Exchange-correlation potentials J. Chem. Phys. 105 9200 10.1063/1.472753
Mortensen J J Kaasbjerg K Frederiksen S L Nørskov J K Sethna J P Jacobsen K W 2005 Bayesian error estimation in density-functional theory Phys. Rev. Lett. 95 216401 10.1103/physrevlett.95.216401
Snyder J C Rupp M Hansen K Müller K-R Burke K 2012 Finding density functionals with machine learning Phys. Rev. Lett. 108 253002 10.1103/physrevlett.108.253002
Nagai R Akashi R Sugino O 2020 Completing density functional theory by machine learning hidden messages from molecules npj Comput. Mater. 6 43 10.1038/s41524-020-0310-0
Li L Hoyer S Pederson R Sun R Cubuk E D Riley P Burke K 2021 Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics Phys. Rev. Lett. 126 036401 10.1103/physrevlett.126.036401
Brockherde F Vogt L Li L Tuckerman M E Burke K Müller K-R 2017 Bypassing the Kohn-Sham equations with machine learning Nat. Commun. 8 872 10.1038/s41467-017-00839-3
Dick S Fernandez-Serra M 2020 Machine learning accurate exchange and correlation functionals of the electronic density Nat. Commun. 11 3509 10.1038/s41467-020-17265-7
Kasim M F Vinko S M 2021 Learning the exchange-correlation functional from nature with fully differentiable density functional theory Phys. Rev. Lett. 127 126403 10.1103/physrevlett.127.126403
Lu D Wang H Chen M Lin L Car R E W Jia W Zhang L Zhang L 2021 86 PFLOPS deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy Comput. Phys. Commun. 259 107624 10.1016/j.cpc.2020.107624
Mardirossian N Head-Gordon M 2017 Mol. Phys. 115 2315 2372 2315-72 10.1080/00268976.2017.1333644
Gillan M J Alfè D Michaelides A 2016 J. Chem. Phys. 144 130901 10.1063/1.4944633
Nagai R Akashi R Sasaki S Tsuneyuki S 2018 J. Chem. Phys. 148 241737 10.1063/1.5029279
Schmidt J Benavides-Riveros C L Marques M A L 2019 J. Phys. Chem. Lett. 10 6425 6431 6425-31 10.1021/acs.jpclett.9b02422
Kanungo B Zimmerman P M Gavini V 2019 Nat. Commun. 10 4497 10.1038/s41467-019-12467-0
Lin S-C Martius G Oettel M 2020 J. Chem. Phys. 152 021102 10.1063/1.5135919
Carleo G Troyer M 2017 Science 355 602 606 602-6 10.1126/science.aag2302
Luo D Clark B K 2019 Phys. Rev. Lett. 122 226401 10.1103/physrevlett.122.226401
Han J Zhang L E W 2019 J. Comput. Phys. 399 108929 10.1016/j.jcp.2019.108929
Hermann J Schätzle Z Noé F 2020 Nat. Chem. 12 891 897 891-7 10.1038/s41557-020-0544-y
Pfau D Spencer J S Matthews A G D G Foulkes W M C 2020 Phys. Rev. Res. 2 033429 10.1103/physrevresearch.2.033429
Al-Hamdani Y S Nagy P R Zen A Barton D Kállay M Brandenburg J G Tkatchenko A 2021 Nat. Commun. 12 3927 10.1038/s41467-021-24119-3
Schätzle Z Hermann J Noé F 2021 J. Chem. Phys. 154 124108 10.1063/5.0032836
Hutter M 2020 On representing (anti)symmetric functions (arXiv: 2007.15298)
Choo K Mezzacapo A Carleo G 2020 Nat. Commun. 11 2368 10.1038/s41467-020-15724-9
Mills K Spanner M Tamblyn I 2017 Deep learning and the Schrödinger equation Phys. Rev. A 96 042113 10.1103/physreva.96.042113
Pilati S Pieri P 2019 Supervised machine learning of ultracold atoms with speckle disorder Sci. Rep. 9 5613 10.1038/s41598-019-42125-w
Carleo G Troyer M 2017 Solving the quantum many-body problem with artificial neural networks Science 355 602 10.1126/science.aag2302
Pilati S Pieri P 2020 Simulating disordered quantum Ising chains via dense and sparse restricted Boltzmann machines Phys. Rev. E 101 063308 10.1103/physreve.101.063308
Ryczko K Strubbe D A Tamblyn I 2019 Deep learning and density-functional theory Phys. Rev. A 100 022512 10.1103/physreva.100.022512
Denner M M Fischer M H Neupert T 2020 Efficient learning of a one-dimensional density functional theory Phys. Rev. Res. 2 033388 10.1103/physrevresearch.2.033388
Nelson J Tiwari R Sanvito S 2019 Machine learning density functional theory for the Hubbard model Phys. Rev. B 99 075132 10.1103/physrevb.99.075132
Mills K Ryczko K Luchak I Domurad A Beeler C Tamblyn I 2019 Extensive deep neural networks for transferring small scale learning to large scale systems Chem. Sci. 10 4129 10.1039/c8sc04578j
Saraceni N Cantori S Pilati S 2020 Scalable neural networks for the efficient learning of disordered quantum systems Phys. Rev. E 102 033301 10.1103/physreve.102.033301
Mujal P Marques M A L Polls A Juliá-Díaz B Pilati S 2021 Supervised learning of few dirty bosons with variable particle number SciPost Phys. 10 073 10.21468/scipostphys.10.3.073
Ryczko K Wetzel S J Melko R G Tamblyn I 2021 Orbital-free density functional theory with small datasets and deep learning J. Chem. Theory Comput. 18 1122 1128 1122-8 10.1021/acs.jctc.1c00812
Draxl C Scheffler M 2019 The NOMAD laboratory: from data sharing to artificial intelligence J. Phys. Mater. 2 036001 10.1088/2515-7639/ab13bb
Wilkinson M D 2016 The FAIR guiding principles for scientific data management and stewardship Sci. Data 3 160018 10.1038/sdata.2016.18
Lejaeghere K 2016 Reproducibility in density-functional theory calculations of solids Science 351 aad3000 10.1126/science.aad3000
Gulans A Kozhevnikov A Draxl C 2018 Microhartree precision in density functional theory calculations Phys. Rev. B 97 161105(R) 10.1103/physrevb.97.161105
Jensen S R Saha S Flores-Livas J A Huhn W Blum V Goedecker S Frediani L 2017 The elephant in the room of density functional theory calculations J. Phys. Chem. Lett. 8 1449 10.1021/acs.jpclett.7b00255
Nabok D Gulans A Draxl C 2016 Accurate all-electron G 0 W 0 quasiparticle energies employing the full-potential augmented planewave method Phys. Rev. B 94 035418 10.1103/physrevb.94.035118
Rangel T 2020 Reproducibility in G0W0 calculations for solids Comput. Phys. Commun. 255 107242 10.1016/j.cpc.2020.107242
Gulans A Draxl C 2022 Influence of spin-orbit coupling on chemical bonding (arXiv: 2204.02751)
Carbogno C 2020 Numerical quality control for DFT-based materials databases Npj Comput. Mater. (arXiv: 2008.10402)
Kuban M Gabaj S Aggoune W Vona C Rigamonti S Draxl C 2022 Similarity of materials and data-quality assessment by fingerprinting (arXiv: 2204.04056)
Scheffler M 2022 FAIR data enabling new horizons for materials research Nature 604 635 642 635-42 10.1038/s41586-022-04501-x
Isayev O Fourches D Muratov E N Oses C Rasch K Tropsha A Curtarolo S 2015 Materials cartography: representing and mining materials space using structural and electronic fingerprints Chem. Mater. 27 735 10.1021/cm503507h
Kuban M Rigamonti S Scheidgen M Draxl C 2022 Density-of-states similarity descriptor for unsupervised learning from materials data (arXiv: 2201.02187)
Data from the NOMAD Laboratory [187]; exciting data: 10.17172/NOMAD/2018.08.22, PID 553302; VASP data: PID 69356; FHI-aims: 10.17172/NOMAD/2018.10.05-1, PID: 5842781.
Toher C 2018 The AFLOW fleet for materials discovery Handbook of Materials Modeling Andreoni W Yip S Berlin Springer 1 28 pp 1-28
Hicks D Mehl M J Gossett E Toher C Levy O Hanson R M Hart G Curtarolo S 2019 The AFLOW library of crystallographic prototypes: part 2 Comput. Mater. Sci. 161 S1 S1011 S1-1011 10.1016/j.commatsci.2018.10.043
Hicks D Oses C Gossett E Gomez G Taylor R H Toher C Mehl M J Levy O Curtarolo S 2018 AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals Acta Crystallogr. A 74 184 203 184-203 10.1107/s2053273318003066
Oses C 2018 AFLOW-CHULL: cloud-oriented platform for autonomous phase stability analysis J. Chem. Inf. Model. 58 2477 2490 2477-90 10.1021/acs.jcim.8b00393
Isayev O Oses C Toher C Gossett E Curtarolo S Tropsha A 2017 Universal fragment descriptors for predicting properties of inorganic crystals Nat. Commun. 8 15679 10.1038/ncomms15679
Gossett E 2018 AFLOW-ML: a RESTful API for machine-learning predictions of materials properties Comput. Mater. Sci. 152 134 145 134-45 10.1016/j.commatsci.2018.03.075
Rose F Toher C Gossett E Oses C Buongiorno Nardelli M Fornari M Curtarolo S 2017 AFLUX: the LUX materials search API for the AFLOW data repositories Comput. Mater. Sci. 137 362 370 362-70 10.1016/j.commatsci.2017.04.036
Hicks D Toher C Ford D C Rose F De Santo C Levy O Mehl M J Curtarolo S 2021 AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes npj Comput. Mater. 7 30 10.1038/s41524-020-00483-4
Friedrich R Usanmaz D Oses C Supka A Fornari M Buongiorno Nardelli M Toher C Curtarolo S 2019 Coordination corrected ab initio formation enthalpies npj Comput. Mater. 5 59 10.1038/s41524-019-0192-1
Yang K Oses C Curtarolo S 2016 Modeling off-stoichiometry materials with a high-throughput ab initio approach Chem. Mater. 28 6484 6492 6484-92 10.1021/acs.chemmater.6b01449
Balachandran P V 2020 Adaptive machine learning for efficient materials design MRS Bull. 45 579 586 579-86 10.1557/mrs.2020.163
Saal J E Oliynyk A O Meredig B 2020 Machine learning in materials discovery: confirmed predictions and their underlying approaches Annu. Rev. Mater. Res. 50 49 69 49-69 10.1146/annurev-matsci-090319-010954
Lookman T Balachandran P V Xue D Yuan R 2019 Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design npj Comput. Mater. 5 180 10.1038/s41524-019-0153-8
Balachandran P V Xue D Theiler J Hogden J Lookman T 2016 Adaptive strategies for materials design using uncertainties Sci. Rep. 6 19660 10.1038/srep19660
Settles B 2012 Active learning Synthesis Lectures on Artificial Intelligence and Machine Learning vol 6 1 114 1-114
Yu M Yang S Wu C Marom N 2020 Machine learning the Hubbard U parameter in DFT + U using Bayesian optimization npj Comput. Mater. 6 180 10.1038/s41524-020-00446-9
Herbol H C Hu W Frazier P Clancy P Poloczek M 2018 Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization npj Comput. Mater. 4 51 10.1038/s41524-018-0106-7
Ju S Shiga T Feng L Hou Z Tsuda K Shiomi J 2017 Designing nanostructures for phonon transport via Bayesian optimization Phys. Rev. X 7 021024 10.1103/physrevx.7.021024
Wolpert D H 2002 The supervised learning no-free-lunch theorems Soft Computing and Industry Roy R Köppen M Ovaska S Furuhashi T Hoffmann F Berlin Springer 25 42 pp 25-42
Balachandran P V Kowalski B Sehirlioglu A Lookman T 2018 Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning Nat. Commun. 9 1668 10.1038/s41467-018-03821-9
Henderson A N Kauwe S K Sparks T D 2021 Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics Data Brief 37 107262 10.1016/j.dib.2021.107262
Sutton R S Barto A G 2018 Reinforcement Learning: An Introduction Cambridge, MA MIT Press
Bellemare M G Candido S Castro P S Gong J Machado M C Moitra S Ponda S S Wang Z 2020 Nature 588 77 82 77-82 10.1038/s41586-020-2939-8
Gottipati S K 2020 (arXiv: 2010.03744)
Simm G N C Pinsler R Miguel Hernández-Lobato J 2020 Proc. 37th Int. Conf. Machine Learning Vienna, Austria
Gottipati S K 2020 (arXiv: 2004.12485)
Thiede L A Krenn M Nigam A Aspuru-Guzik A 2020 (arXiv: 2012.11293)
Gaudin T Nigam A Aspuru-Guzik A 2019 2nd Workshop on Machine Learning and the Physical Sciences NeurIPS Vancouver, Canada
Kober J Bagnell J A Peters J 2013 Int. J. Robot. Res. 32 1238 1274 1238-74 10.1177/0278364913495721
Yang Y 2018 Proc. 27th Int. Joint Conf. Artificial Intelligence (IJCAI-18) 5739 5743 5739-43
Zhao W Peña Queralta J Westerlund T 2020 arXiv: 2009.13303
Lipton Z C 2018 The mythos of model interpretability Queue 16 31 57 31-57 10.1145/3236386.3241340
Murdoch W J Singh C Kumbier K Abbasi-Asl R Yu B 2019 Definitions, methods, and applications in interpretable machine learning Proc. Natl Acad. Sci. USA 116 22071 22080 22071-80 10.1073/pnas.1900654116
Ribana R Bohn B Duarte M F Garcke J 2020 Explainable machine learning for scientific insights and discoveries IEEE Access 8 42200 42216 42200-16 10.1109/ACCESS.2020.2976199
Doshi-Velez F Kim B 2017 Towards a rigorous science of interpretable machine learning (arXiv: 1702.08608)
Gilpin L H Bau D Yuan B Z Bajwa A Specter M Kagal L 2018 Explaining explanations: an overview of interpretability of machine learning 2018 IEEE 5th Int. Conf. Data Science and Advanced Analytics (DSAA) IEEE 80 89 80-9
Carvalho D V Pereira E M Cardoso. J S 2019 Machine learning interpretability: a survey on methods and metrics Electronics 8 832 10.3390/electronics8080832
Nori H Jenkins S Koch P Caruana R 2019 Interpretml: a unified framework for machine learning interpretability (arXiv: 1909.09223)
Tibshirani R 1996 Regression shrinkage and selection via the lasso J. Roy. Stat. Soc. B 58 267 288 267-88 10.1111/j.2517-6161.1996.tb02080.x
Wang Y Wagner N Rondinelli J M 2019 Symbolic regression in materials science MRS Commun. 9 793 805 793-805 10.1557/mrc.2019.85
Sutton C Boley M Ghiringhelli L M Rupp M Vreeken J Scheffler M 2020 Identifying domains of applicability of machine learning models for materials science Nat. Commun. 11 4428 10.1038/s41467-020-17112-9
