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On representing chemical environments
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We review some recently published methods to represent atomic neighborhood environments, and analyze their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces. The crucial properties that such representations (sometimes called descriptors) must have are differentiability with respect to moving the atoms and invariance to the basic symmetries of physics: rotation, reflection, translation, and permutation of atoms of the same species. We demonstrate that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighborhood density function. Using the example system of small clusters, we quantitatively show that this expansion needs to be carried to higher and higher wave numbers as the number of neighbors increases in order to obtain a faithful representation, and that variants of the descriptors converge at very different rates. We also propose an altogether different approach, called Smooth Overlap of Atomic Positions, that sidesteps these difficulties by directly defining the similarity between any two neighborhood environments, and show that it is still closely connected to the invariant descriptors. We test the performance of the various representations by fitting models to the potential energy surface of small silicon clusters and the bulk crystal.
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“General-purpose and systematically-convergent descriptions of chemical environments, such as those that are typically used to fit potentials or predict molecular properties 31,35 , provide a starting point that is considerably less biased than descriptors based on complicated heuristics that are designed from the outset to identify e.g. a stacking order, or the coordination of a site.”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [31]
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Cites
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Information Theory, Inference, and Learning Algorithms
(2004) · Kybernetes · doi:10.1108/03684920410534506 · Cited by 4,937
Quantum Theory of Angular Momentum
Варшалович, Д. А., Moskalev, A. N., & Khersonskii, V. K. (1988) · WORLD SCIENTIFIC eBooks · doi:10.1142/0270 · Cited by 3,980
Bond-orientational order in liquids and glasses
Steinhardt, P. J., Nelson, D. R., & Ronchetti, M. (1983) · Physical review. B, Condensed matter · doi:10.1103/physrevb.28.784 · Cited by 3,511
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
Self-citationQuantum Theory Of Angular Momemtum
Варшалович, Д. А., Moskalev, A. N., & Khersonskii, V. K. (1988) · Cited by 3,060
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
Porezag, D. V., Frauenheim, T., Köhler, T., Seifert, G., & Kaschner, R. (1995) · Physical review. B, Condensed matter · doi:10.1103/physrevb.51.12947 · Cited by 2,191
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Behler, J. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3553717 · Cited by 1,663
The Classical Groups: Their Invariants and Representations
Weyl, H. (1947) · doi:10.1515/9781400883905 · Cited by 1,603
Research works printed but not linked: 1
- H. Weyl, 1946, The Classical Groups
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Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786
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Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217
Wang, H., Zhang, L., Han, J., & Weinan, E. (2018) · Computer Physics Communications · doi:10.1016/j.cpc.2018.03.016 · Cited by 2,213
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Shapeev, A. V. (2016) · Multiscale Modeling and Simulation · doi:10.1137/15m1054183 · Cited by 1,506
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,489
Gaussian Process Regression for Materials and Molecules
Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Cśanyi, G. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00022 · Cited by 1,311
Self-citationRecent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K. (2017) · Science Advances · doi:10.1126/sciadv.1603015 · Cited by 1,237
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M., & Tucker, G. J. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.12.018 · Cited by 1,122
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
Deringer, V. L., Caro, M. A., & Cśanyi, G. (2019) · Advanced Materials · doi:10.1002/adma.201902765 · Cited by 1,136
Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.014104 · Cited by 1,056
Machine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
Graph neural networks for materials science and chemistry
Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al. (2022) · Communications Materials · doi:10.1038/s43246-022-00315-6 · Cited by 976
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 942
Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z., & Farimani, A. B. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00447-x · Cited by 966
DScribe: Library of descriptors for machine learning in materials science
Himanen, L., Jäger, M. O. J., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., & Foster, A. S. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.106949 · Cited by 901
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778
Self-citationMachine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764
Self-citationTowards exact molecular dynamics simulations with machine-learned force fields
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Learning local equivariant representations for large-scale atomistic dynamics
Musaelian, A., Batzner, S., Johansson, A., Sun, L., Owen, C. J., Kornbluth, M., & Kozinsky, B. (2023) · Nature Communications · doi:10.1038/s41467-023-36329-y · Cited by 847
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Behler, J. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201703114 · Cited by 713
On-the-fly machine learning force field generation: Application to melting points
Jinnouchi, R., Karsai, F., & Kresse, G. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.100.014105 · Cited by 680
Active learning of linearly parametrized interatomic potentials
Podryabinkin, E. V., & Shapeev, A. V. (2017) · Computational Materials Science · doi:10.1016/j.commatsci.2017.08.031 · Cited by 704
Machine learning based interatomic potential for amorphous carbon
Deringer, V. L., & Cśanyi, G. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.95.094203 · Cited by 696
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 656
Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G., & Bokdam, M. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.225701 · Cited by 635
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Li, Z., Kermode, J. R., & Vita, A. D. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.096405 · Cited by 628
CREST—A program for the exploration of low-energy molecular chemical space
Pracht, P., Grimme, S., Bannwarth, C., Bohle, F., Ehlert, S., Feldmann, G., Gorges, J., Müller, M., Neudecker, T., Plett, C., et al. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0197592 · Cited by 618
Gaussian approximation potentials: A brief tutorial introduction
Bartók, A. P., & Csányi, G. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24927 · Cited by 617
Self-citationA Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
The MLIP package: moment tensor potentials with MPI and active learning
Novikov, I. S., Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abc9fe · Cited by 631
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Mai, H., Le, T. C., Chen, D., Winkler, D. A., & Caruso, R. A. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00061 · Cited by 588
Yao, N., Chen, X., Fu, Z., & Zhang, Q. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00904 · Cited by 583
Machine-learned potentials for next-generation matter simulations
Friederich, P., Häse, F., Proppe, J., & Aspuru‐Guzik, A. (2021) · Nature Materials · doi:10.1038/s41563-020-0777-6 · Cited by 595
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Cśanyi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 594
Self-citationOpportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
Fan, Z., Zeng, Z., Zhang, C., Wang, Y., Song, K., Dong, H., Chen, Y., & Ala-Nissilä, T. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.104309 · Cited by 547
Rosen, A., Iyer, S. M., Ray, D., Yao, Z., Aspuru‐Guzik, A., Gagliardi, L., Notestein, J. M., & Snurr, R. Q. (2021) · Matter · doi:10.1016/j.matt.2021.02.015 · Cited by 571
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Yao, Z., Lum, Y., Johnston, A. K., Mejía-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru‐Guzik, A., Sargent, E. H., & Seh, Z. W. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00490-5 · Cited by 571
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Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., Fabritiis, G. D., Noé, F., & Clementi, C. (2019) · ACS Central Science · doi:10.1021/acscentsci.8b00913 · Cited by 552
Machine Learning Force Fields: Construction, Validation, and Outlook
Botu, V., Batra, R., Chapman, J., & Ramprasad, R. (2016) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b10908 · Cited by 536
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo‐Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 536
Artrith, N., & Urban, A. (2016) · Computational Materials Science · doi:10.1016/j.commatsci.2015.11.047 · Cited by 521
Machine Learning a General-Purpose Interatomic Potential for Silicon
Bartók, A. P., Kermode, J. R., Bernstein, N., & Cśanyi, G. (2018) · Physical Review X · doi:10.1103/physrevx.8.041048 · Cited by 514
Self-citationFan, Z., Wang, Y., Ying, P., Song, K., Wang, J., Wang, Y., Zeng, Z., Xu, K. W., Lindgren, E., Rahm, J. M., et al. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0106617 · Cited by 515
Zhang, Y., Wen, C., Wang, C., Antonov, S., Xue, D., Bai, Y., & Su, Y. (2019) · Acta Materialia · doi:10.1016/j.actamat.2019.11.067 · Cited by 473
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. T., Kindermans, P.-J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., & Müller, K. (2017) · Open Repository and Bibliography (University of Luxembourg) · doi:10.48550/arxiv.1706.08566 · Cited by 463
Schütt, K. T., Glawe, H., Brockherde, F., Sanna, A., Müller, K. R., & Gross, E. K. U. (2014) · Physical Review B · doi:10.1103/physrevb.89.205118 · Cited by 467
On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M., & Kozinsky, B. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0283-z · Cited by 459
Crystal structure representations for machine learning models of formation energies
Faber, F. A., Lindmaa, A., von Lilienfeld, O. A., & Armiento, R. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24917 · Cited by 466
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Machine learning for heterogeneous catalyst design and discovery
Goldsmith, B. R., Esterhuizen, J. A., Liu, J., Bartel, C. J., & Sutton, C. A. (2018) · AIChE Journal · doi:10.1002/aic.16198 · Cited by 460
The Role of Machine Learning in the Understanding and Design of Materials
Moosavi, S. M., Jablonka, K. M., & Smit, B. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c09105 · Cited by 451
Machine Learning for Chemical Reactions
Meuwly, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00033 · Cited by 448
Machine Learning for Electronically Excited States of Molecules
Westermayr, J., & Marquetand, P. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00749 · Cited by 445
Machine learning for high-entropy alloys: Progress, challenges and opportunities
Liu, X., Zhang, J., & Pei, Z. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101018 · Cited by 434
Goodall, R. E. A., & Lee, A. A. (2020) · Nature Communications · doi:10.1038/s41467-020-19964-7 · Cited by 433
Unsupervised Learning Methods for Molecular Simulation Data
Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 436
Ward, L. T., Liu, R., Krishna, A., Hegde, V. I., Agrawal, A., Choudhary, A., & Wolverton, C. M. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.96.024104 · Cited by 417
Inverse design of nanoporous crystalline reticular materials with deep generative models
Yao, Z., Sánchez-Lengeling, B., Bobbitt, N. S., Bucior, B. J., Kumar, S. G. H., Collins, S. P., Burns, T. D., Woo, T. K., Farha, O. K., Snurr, R. Q., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00271-1 · Cited by 429
Machine-Learning Interatomic Potentials for Materials Science
Mishin, Y. M. (2021) · Acta Materialia · doi:10.1016/j.actamat.2021.116980 · Cited by 431
Podryabinkin, E. V., Tikhonov, E., Shapeev, A. V., & Оганов, А. Р. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.064114 · Cited by 408
Unified representation of molecules and crystals for machine learning
Huo, H., & Rupp, M. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aca005 · Cited by 419
Representing potential energy surfaces by high-dimensional neural network potentials
Behler, J. (2014) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/26/18/183001 · Cited by 408
Adaptive machine learning framework to accelerate ab initio molecular dynamics
Botu, V., & Ramprasad, R. (2014) · International Journal of Quantum Chemistry · doi:10.1002/qua.24836 · Cited by 399
Exploring chemical compound space with quantum-based machine learning
von Lilienfeld, O. A., Müller, K., & Tkatchenko, A. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0189-9 · Cited by 396
Insightful classification of crystal structures using deep learning
Ziletti, A., Kumar, D., Scheffler, M., & Ghiringhelli, L. M. (2018) · Nature Communications · doi:10.1038/s41467-018-05169-6 · Cited by 383
Machine learning: Accelerating materials development for energy storage and conversion
Chen, A., Zhang, X., & Zhou, Z. P. (2020) · InfoMat · doi:10.1002/inf2.12094 · Cited by 381
Sieving carbons promise practical anodes with extensible low-potential plateaus for sodium batteries
Li, Q., Liu, X., Tao, Y., Huang, J., Zhang, J., Yang, C., Zhang, Y., Zhang, S., Jia, Y., Lin, Q., et al. (2022) · National Science Review · doi:10.1093/nsr/nwac084 · Cited by 364
Alchemical and structural distribution based representation for universal quantum machine learning
Faber, F. A., Christensen, A. S., Huang, B., & von Lilienfeld, O. A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5020710 · Cited by 372
Machine learning for interatomic potential models
Mueller, T., Hernández, A., & Wang, C. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5126336 · Cited by 380
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
Machine learning for quantum mechanics in a nutshell
Rupp, M. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24954 · Cited by 366
Machine Learning for Computational Heterogeneous Catalysis
Schlexer, P., Winther, K. T., Torres, J. A. G., Streibel, V., Zhao, M., Bajdich, M., Abild‐Pedersen, F., & Bligaard, T. (2019) · ChemCatChem · doi:10.1002/cctc.201900595 · Cited by 362
Amp: A modular approach to machine learning in atomistic simulations
Khorshidi, A., & Peterson, A. A. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2016.05.010 · Cited by 366
The NOMAD laboratory: from data sharing to artificial intelligence
Draxl, C., & Scheffler, M. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab13bb · Cited by 388
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Unke, O. T., Chmiela, S., Gastegger, M., Schütt, K. T., Sauceda, H. E., & Müller, K. (2021) · Nature Communications · doi:10.1038/s41467-021-27504-0 · Cited by 379
The design space of E(3)-equivariant atom-centred interatomic potentials
Batatia, I., Batzner, S., Kovács, D. P., Musaelian, A., Simm, G. N. C., Drautz, R., Ortner, C., Kozinsky, B., & Cśanyi, G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00956-x · Cited by 435
Artrith, N., Urban, A., & Ceder, G. (2017) · Physical review. B./Physical review. B · doi:10.1103/physrevb.96.014112 · Cited by 331
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Metadata
- Authors
- Bartók, A. P., Kondor, R., & Csányi, G.
- Affiliations
- University of Cambridge (GB)
- University of Chicago (US)
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2013-05-28
- Volume
- 87
- Issue
- 18
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2029413789
- Cited by
- 2,572
- References
- 60
- Citations by year
- 2026: 292 · 2025: 350 · 2024: 367 · 2023: 365 · 2022: 303 · 2021: 270 · 2020: 257 · 2019: 163 · 2018: 105 · 2017: 48 · 2016: 19 · 2015: 11 · 2014: 13 · 2013: 4
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review B (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/K014560/1)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- potential energy surface fitting, basis function expansion, silicon clusters, small clusters
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