4.7 Article

Machine Learning a General-Purpose Interatomic Potential for Silicon

期刊

PHYSICAL REVIEW X
卷 8, 期 4, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevX.8.041048

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资金

  1. Office of Naval Research through the U.S. Naval Research Laboratory's core basic research program
  2. Engineering and Physical Sciences Research Council (EPSRC) [EP/P002188/1]
  3. Royal Society [RG160691]
  4. Magdalene College, Cambridge
  5. Leverhulme Early Career Fellowship
  6. Isaac Newton Trust
  7. Collaborative Computational Project for NMR Crystallography (CCP-NC)
  8. UKCP Consortium - EPSRC [EP/M022501/1, EP/P022561/1]
  9. Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program
  10. DOE Office of Science User Facility [DE-AC02-06CH11357]
  11. EPSRC [EP/P022561/1, EP/K014560/1]
  12. EPSRC [EP/K014560/1, EP/L027682/1, EP/M022501/1, EP/P022596/1, EP/R012474/1, EP/P002188/1, EP/P022561/1] Funding Source: UKRI

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The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is constrained by the limitations on simulated length scales and timescales due to the computational cost and its scaling. Techniques based on machine-learning ideas for interpolating the Born-Oppenheimer potential energy surface without explicitly describing electrons have recently shown great promise, but accurately and efficiently fitting the physically relevant space of configurations remains a challenging goal. Here, we present a Gaussian approximation potential for silicon that achieves this milestone, accurately reproducing density-functional-theory reference results for a wide range of observable properties, including crystal, liquid, and amorphous bulk phases, as well as point, line, and plane defects. We demonstrate that this new potential enables calculations such as finite-temperature phase-boundary lines, self-diffusivity in the liquid, formation of the amorphous by slow quench, and dynamic brittle fracture, all of which are very expensive with a first-principles method. We show that the uncertainty quantification inherent to the Gaussian process regression framework gives a qualitative estimate of the potential's accuracy for a given atomic configuration. The success of this model shows that it is indeed possible to create a useful machine-learning-based interatomic potential that comprehensively describes a material on the atomic scale and serves as a template for the development of such models in the future.

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