Machine Learning a General-Purpose Interatomic Potential for Silicon
Machine Learning a General-Purpose Interatomic Potential for Silicon
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DOI：
10.1103/physrevx.8.041048
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发表时间：
2018-12-14
影响因子：
12.5
通讯作者：
Csanyi, Gabor
中科院分区：
文献类型：
--
作者：
Bartok, Albert P.;Kermode, James;Csanyi, Gabor
第一性原理电子结构计算在化学、固体物理和材料科学中用于预测建模的成功受到模拟长度尺度和时间尺度的限制，这是由于计算成本及其尺度。基于机器学习思想的插补Born-Oppenheimer势能面而不显式描述电子的技术最近显示出巨大的前景，但准确和高效地拟合物理相关的组态空间仍然是一个具有挑战性的目标。在这里，我们提出了实现这一里程碑的硅的高斯近似势，准确地再现了广泛可观察到的性质的密度泛函理论参考结果，包括晶体、液体和非晶体相，以及点、线和面缺陷。我们证明，这种新的势可以计算有限温度相边界线、液体中的自扩散系数、慢冷非晶态的形成和动态脆性断裂，所有这些都是用第一性原理方法非常昂贵的。我们表明，高斯过程回归框架固有的不确定性量化给出了对给定原子构型的势的精度的定性估计。这一模型的成功表明，确实有可能创建一种基于机器学习的有用的原子间势，它在原子尺度上全面描述一种材料，并作为未来开发此类模型的模板。
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.