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半实证模型和机器学习在计算化学中的协同作用

Nikita Fedik^1,2, Benjamin Nebgen¹, Nicholas Lubbers³

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

The Journal of chemical physics

|September 15, 2023

概括

化学中的机器学习与新的化学空间和空间局部性作斗争. 这项研究提出了使用机器学习来纠正半经验量子力学的物理知情模型,以准确和高效的材料科学应用.

科学领域:

计算化学和材料科学计算化学和材料科学
机器学习在科学发现中的应用

背景情况:

数据驱动的机器学习 (ML) 模型显示出有希望的结果,但由于空间局部假设等局限性,当将其推断到新的化学空间时会出现动摇.
当前的替代模型,如原子间潜能,往往缺乏关键的电子结构信息,阻碍了它们的可转移性和准确性.
虽然机器学习和计算化学正在进步,但空间局部约束预计将继续作为模型通用性的障碍.

研究的目的:

通过开发基于物理的模型来解决化学中纯数据驱动的ML模型的局限性.
将领域知识与ML整合起来,将ML用作纠正工具,而不是独立的预测模型.
提高半经验量子力学方法在材料科学中的准确性和适用性.

主要方法:

专注于半经验量子力学 (SEQM) 作为开发基于物理的模型的基础.
在SEQM框架内使用机器学习来预测减少顺序的哈密尔顿模型参数的校正.
利用量子力学和材料特性领域的知识来指导ML模型的开发.

主要成果:

开发的基于物理的模型显示了各种化学系统的广泛适用性.
这些模型的计算速度与传统的半经验化学方法相美.
建议模型的准确性经常与计算上昂贵的初始计算相匹配.

结论:

基于物理的模型,将领域知识与ML结合起来,为化学中纯数据驱动方法的局限性提供了可行的解决方案.
在SEQM等已建立的基于物理的框架中将ML作为纠正工具集成到ML中,可以产生高度准确和高效的模型.
机器学习和量子化学方法的联合开发具有在化学应用中实现高精度和数值效率的巨大潜力.

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