稀疏高斯过程电位:锂在超离子导电固体电解质中的扩散率研究

Amir Hajibabaei, C. Myung, Kwang Soo Kim
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引用次数: 14

摘要

对于原子间势的机器学习,引入了稀疏高斯过程回归形式和数据高效的自适应采样算法。这适用于几十种固体电解质。作为一个展示,Li7P3S11再现了实验熔融和玻璃结晶温度,揭示了一个未知的非晶相,其Li扩散率要低得多,这应该被规避。通过专家模型的层次化组合,生成了通用势,为用组合方法建模大规模复杂性铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes
For machine learning of interatomic potentials the sparse Gaussian process regression formalism is introduced with a data-efficient adaptive sampling algorithm. This is applied for dozens of solid electrolytes. As a showcase, experimental melting and glass-crystallization temperatures are reproduced for Li7P3S11 and an unchartered infelicitous phase is revealed with much lower Li diffusivity which should be circumvented. By hierarchical combinations of the expert models universal potentials are generated, which pave the way for modeling large-scale complexity by a combinatorial approach.
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