The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning scheme to predict atomic forces and accelerate materials simulations
Open accessAbstract
The behavior of an atom in a molecule, liquid, or solid is governed by the force it experiences. If the dependence of this vectorial force on the atomic chemical environment can be learned efficiently with high fidelity from benchmark reference results---using ``big-data'' techniques, i.e., without resorting to actual functional forms---then this capability can be harnessed to enormously speed up in silico materials simulations. The present contribution provides several examples of how such a force field for Al can be used to go far beyond the length-scale and time-scale regimes presently accessible using quantum-mechanical methods. It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements.
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Cites
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
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
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
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
Cited by
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
Self-citationPerformance 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
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
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 678
The MLIP package: moment tensor potentials with MPI and active learning
Novikov, I. S., Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc9fe · Cited by 587
Metadata
- Authors
- Botu, V., & Ramprasad, R.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2015-09-25
- Volume
- 92
- Issue
- 9
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2225949634
- Cited by
- 202
- References
- 4
- FWCI
- 7.99
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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