The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
Open accessAbstract
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this paper, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.
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Cites
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
Self-citationGaussian 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
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
Metrics for measuring distances in configuration spaces
Sadeghi, A., Ghasemi, S. A., Schaefer, B., Mohr, S., Lill, M. A., & Goedecker, S. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4828704 · Cited by 141
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Recent advances and applications of machine learning in solid-state materials science
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 2,607
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
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Unke, O. T., & Meuwly, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00181 · Cited by 1,073
Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R. (2019) · Physical Review B · doi:10.1103/physrevb.99.014104 · Cited by 959
Metadata
- Authors
- Artrith, N., Urban, A., & Ceder, G.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2017-07-21
- Volume
- 96
- Issue
- 1
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2650911154
- Cited by
- 340
- References
- 7
- FWCI
- 12.87
- Type
- journal article
- Language
- en
- Open access
- green
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