.png){kind=icon}
返回
Machine Learning a General-Purpose Interatomic Potential for Silicon
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevX.8.041048.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
DENSITY-FUNCTIONAL THEORY
NEURAL-NETWORK POTENTIALS
EMBEDDED-ATOM POTENTIALS
TIGHT-BINDING MODEL
MOLECULAR-DYNAMICS
THERMAL-EXPANSION
MAGIC NUMBERS
CONCERTED EXCHANGE
ENERGY SURFACES
STACKING-FAULTS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
2.7K
被引数:
3.4W
机构
引用论文
.png){kind=icon}
分享收藏Job tenure and quality of work life of people with psychiatric disabilities working in social enterprises在社会企业工作的精神病患者的工作任期和工作生活质量
PREAI
分享收藏Fe–N bonding in a carbon nanotube–graphene complex for oxygen reduction: an XAS study用于氧还原的碳纳米管-石墨烯复合物中的fe-n键: XAS研究
PREAI
分享收藏
查看更多内容