The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Active learning of uniformly accurate interatomic potentials for materials simulation
Open accessAbstract
An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.
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Metadata
- Authors
- Zhang, L., Lin, D.-Y., Wang, H., Car, R., & E, W.
- Venue
- Physical Review Materials
- Publisher
- American Physical Society (APS)
- Published
- 2019-02-25
- Volume
- 3
- Issue
- 2
- ISSN
- 2475-9953 (ISSN-L)
- OpenAlex
- W2898391453
- Cited by
- 521
- References
- 47
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- green
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