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在原子描述器中确定超参数,以使用机器学习力量进行高效和强大的分子动力学模拟

Jianbo Lin¹, Ryo Tamura^1,2, Yasunori Futamura^3,4,5

¹Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba 305-0044, Japan. lin.janbo@nims.go.jp.

Physical chemistry chemical physics : PCCP

|June 28, 2023

概括

本研究介绍了一种自动优化原子描述符的方法,用于机器学习力预测. 它可以使用更少的描述符进行准确和强大的模拟,提高效率.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
机器学习机器学习

背景情况:

原子描述符对于材料科学中的机器学习 (ML) 力量预测至关重要.
高维描述符可能会导致过拟合和减少可转移性.
有效和强大的ML模型需要优化描述器集.

研究的目的:

开发一种在原子描述器中自动确定超参数的方法.
以减少描述符的数量来实现准确的ML力预测.
提高分子动力学ML模型的稳定性和可转移性.

主要方法:

提出一种新的方法来自动确定原子描述符的超参数.
专注于确定描述器组件的最佳差异值.
使用双体和新型分割型三体描述符.

主要成果:

在晶体,液体和无形SiO2,SiGe和Si系统中证明了有效性.
通过减少描述符集,实现了精确的机器学习力量.
实现了高效和强大的分子动力学模拟.

结论:

拟议的方法有效地优化了用于ML力预测的原子描述符.
减少描述符集导致更高效和可转移的ML模型.
这种方法促进了材料科学中强大的分子动力学模拟.

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