通过自动发现兼容的表示方式来学习多个潜在能量表面
Yinan Shu1, Zoltan Varga1, Dayou Zhang1
1Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
Journal of chemical theory and computation
|March 21, 2025
概括
一种新的深度神经网络方法 (CDNN) 自动学习多个潜在能量表面 (PES) 和它们的梯度,用于多原子系统. 这种准确而方便的方法有助于模拟复杂的化学动态.
科学领域:
- 计算化学的计算化学
- 化学动力学 化学动力学
- 理论化学 理论化学
背景情况:
- 模拟电子非adiabatic过程需要准确的潜在能量表面 (PESs).
- 为多个合的 PES 开发分析表征在化学动态中是一个重大挑战.
- 现有的方法往往缺乏复杂的多原子系统的自动化或准确性.
研究的目的:
- 引入一种新的,自动的方法来学习多个潜在能量表面 (PES) 和它们的梯度.
- 开发一种深度神经网络方法,以高效准确地表示合的 PES.
- 为化学动力学社区提供一个工具,以建模非adiabatic过程.
主要方法:
- 通过深度神经网络 (CDNN) 方法开发了兼容性.
- 使用几何和潜在能量的数据库作为输入.
- 通过专门的CDNN架构生成一个兼容的潜在能量矩阵 (CPEM).
- 通过对角化和自动差异化获得了分析性亚亚巴特 PES 和梯度.
主要成果:
- 证明了CDNN方法的准确性和自动性.
- 展示了CDNN发现CPEM的能力,CPEM作为一个隐式电子汉密尔顿数.
- 验证了该方法对多原子系统的有效性.
结论:
- 该CDNN方法提供了一个准确和完全自动的方法来学习合PESs.
- 这种技术简化了在多原子系统中对电子非adiabatic过程的建模.
- 预计CDNN对于化学动力学研究中的实际应用非常有价值.
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