The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Towards exact molecular dynamics simulations with machine-learned force fields
Open accessAbstract
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.
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Cites
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Self-citationGeneralized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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Self-citationQuantum-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
Self-citationHansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815
Self-citationComparing molecules and solids across structural and alchemical space
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Cited by
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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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
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
“While the above works are mainly based on NNs, there have also been development of graph neural network force field (GNNFF) framework [117, 118] that bypasses both computational bottlenecks.”
BackgroundA universal graph deep learning interatomic potential for the periodic table
Chen, C., & Ong, S. P. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00349-3 · Cited by 1,140
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Metadata
- Authors
- Chmiela, S., Sauceda, H. E., Müller, K.-R., & Tkatchenko, A.
- Venue
- Nature Communications
- Published
- 2018-09-24
- Volume
- 9
- Issue
- 1
- Pages
- 3887-3887
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2788652239
- Cited by
- 700
- References
- 14
- FWCI
- 19.79
- Type
- journal article
- Language
- en
- Open access
- gold
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