机器学习的远程静电学 原子间潜力比我们想象的要容易得多
Dongjin Kim1, Bingqing Cheng1,2,3,4
1Department of Chemistry, UC Berkeley, Berkeley, California 94720, USA.
The Journal of chemical physics
|February 12, 2026
概括
现代机器学习的原子间潜力 (MLIP) 缺乏远程静电学. 隐藏的Ewald总和框架利用环境依赖的电荷,避免模两可的部分电荷来捕捉这些相互作用.
科学领域:
- 计算材料科学 计算材料科学
- 量子化学是一种量子化学.
- 机器学习 机器学习
背景情况:
- 机器学习原子间潜力 (MLIP) 对于模拟材料至关重要.
- 当前MLIP的一个主要局限性是缺乏远程静电相互作用.
- 这一缺陷限制了它们在接口,电荷转移反应和极性材料等领域的应用.
研究的目的:
- 介绍潜伏埃瓦尔德总结框架,用于将远程静电学纳入MLIP.
- 为捕捉静电相互作用和电响应提炼两个核心设计原则.
- 为了证明框架的灵活性和广泛适用性.
主要方法:
- 开发一种库伦函数形式,包含环境依赖的电荷.
- 避免对密度函数理论 (DFT) 进行明确的培训.
- 增加现有的短程MLIPs与隐藏的Ewald总和框架.
主要成果:
- 隐藏的Ewald总和框架有效地捕捉了远程静电相互作用,电荷和电反应.
- 该框架可以与各种MLIP和费用均衡计划集成.
- 推断或微调双极和波恩有效电荷是可以实现的.
结论:
- 将远程静电学纳入MLIP比以前想象的要简单得多.
- 提出的以物理为导向的设计规则提供了一个广泛适用的解决方案.
- 这一进步为更广泛的材料和现象提供了更可靠的MLIP模拟.
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