基于图形神经网络的机器学习预测的激发状态波函数和能量
Xiang-Yang Liu1, Dongyi Xiao2, Wei-Hai Fang2,3
1College of Chemistry and Material Science, Sichuan Normal University, Chengdu 610068, China.
Journal of chemical theory and computation
|September 4, 2025
概括
这项研究引入了一种机器学习 (ML) 方法,以加快光诱导动态的模拟,准确地捕捉刺激效应. 这种方法显著提高了研究材料复杂光化学的计算效率.
科学领域:
- 计算化学
- 材料科学
- 机器学习应用
背景情况:
- 模拟材料中的光诱导动力学是计算密集的.
- 精确的计算需要考虑激发状态电子结构和激发效应.
- 现有的方法面临着复杂系统的计算成本和可扩展性的挑战.
研究的目的:
- 为非adiabatic分子动力学开发一种机器学习加速方法.
- 在激发状态模拟中整合和有效预测激发效应.
- 为了实现材料中的光动力学过程的准确和可扩展的模拟.
主要方法:
- 使用图形神经网络 (GNN) 架构来预测兴奋状态波函数和能量.
- 从GNN输入的地面状态计算中获取分子轨道信息.
- 将ML预测集成到基于ab initio的最少开关的表面跳跃模拟中.
主要成果:
- ML预测的兴奋状态属性与线性响应时间依赖密度函数理论 (LR-TDDFT) 的结果非常相匹配.
- 与传统方法相比,实现了显著的计算效率增长.
- ML加速模拟准确地复制了ZnPc-C60系统中的兴奋状态动态.
结论:
- 开发的ML方法为激发状态动态提供了通用和可扩展的框架.
- 在模拟光动力学过程,包括激发效应方面表现出高可靠性.
- 在更大的材料系统中研究复杂的光化学,以提高效率.
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