The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Open accessAbstract
We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
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Cites
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Cited by
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-29939-5 · Cited by 2,285
Scientific discovery in the age of artificial intelligence.
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“To improve the accuracy of molecular simulations, an AI-based neural potential that fits expensive yet accurate quantum-mechanical data has been developed to replace traditional force fields 16,168 .”
BackgroundMethodSelf-citationUnke, 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
Recent 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
“com/deepmodeling/deepmd-kit [101, 102] ænet Sol https://github.”
Metadata
- Authors
- Zhang, L., Han, J., Wang, H., Car, R., & E, W.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2018-04-04
- Volume
- 120
- Issue
- 14
- Pages
- 143001-143001
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2742127985
- Cited by
- 2,258
- References
- 9
- FWCI
- 43.74
- Type
- journal article
- Language
- en
- Open access
- green
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