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A universal strategy for the creation of machine learning-based atomistic force fields

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Huan, Tran Doan, Batra, Rohit, Chapman, James, Krishnan, Sridevi, Chen, Lihua, Ramprasad, Rampi·2017·npj Computational Materials
DOI 10.1038/s41524-017-0042-y
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Abstract

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.

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Metadata

Authors
Huan, T. D., Batra, R., Chapman, J., Krishnan, S., Chen, L., & Ramprasad, R.
Venue
npj Computational Materials
Publisher
Springer Science and Business Media LLC
Published
2017-09-18
Volume
3
Issue
1
ISSN
2057-3960 (ISSN-L)
DOI
10.1038/s41524-017-0042-y
OpenAlex
W2755837508
Cited by
230
References
6
FWCI
15.13
Type
journal article
Language
en
Open access
gold
Licence

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Huan, T. D., Batra, R., Chapman, J., Krishnan, S., Chen, L., & Ramprasad, R. (2017). A universal strategy for the creation of machine learning-based atomistic force fields. npj Computational Materials. https://doi.org/10.1038/s41524-017-0042-y

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