通过机器学习构建交换-相关函数
Jiang Wu1,2, Sai-Mang Pun1,2, Xiao Zheng3
1Department of Chemistry, The University of Hong Kong, Hong Kong, China.
The Journal of chemical physics
|September 6, 2023
概括
机器学习,特别是深度学习,通过近似交换相关函数来推进量子力学模拟. 新方法改善了从小到大系统的知识传输,提高了密度函数理论的准确性.
科学领域:
- 计算化学的计算化学
- 量子力学就是量子力学.
- 材料科学 材料科学 材料科学
背景情况:
- 密度函数理论 (DFT) 是量子力学模拟的基石.
- 开发一个通用的交换相关性 (XC) 函数仍然是一个重大挑战.
- 机器学习 (ML) 已经成为近似 XC 函数的有希望的方法.
研究的目的:
- 审查ML在近似XC函数的历史和最近的应用.
- 解决从小到大分子系统的知识可转移性的挑战.
- 探索新的深度学习技术及其在一般框架下的统一.
主要方法:
- 对 XC 函数式近似的早期机器学习努力的审查.
- 由全息电子密度定理启发的基于准局部密度的描述符的应用.
- 使用深度学习模型在高水平的初始分子能量和电子密度数据上进行训练.
- 对范德瓦尔斯相互作用的辅助ML模型的探索.
主要成果:
- 使用准本地描述器,证明了基于ML的XC函数的可转移性.
- 深度学习方法的进步为更准确的能量和电子密度预测提供了方法.
- 建议为DFT中的多种ML应用提供一个统一的框架.
- 成功整合了ML用于建模范德瓦尔斯力.
结论:
- 机器学习,特别是深度学习,提供了强大的工具来克服DFT的局限性.
- 解决可转移性问题是开发普遍适用的ML-XC功能性的关键.
- 拟议的框架为更准确和更有效的量子力学模拟提供了一条道路.
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