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Updated: May 25, 2025

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在固体中推进非adiabatic分子动力学模拟,使用E(3) 等价深神经汉密尔顿人

Changwei Zhang¹, Yang Zhong¹, Zhi-Guo Tao¹

¹Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai, 200433, China.

Nature communications

|February 27, 2025

概括

我们介绍了神经网络非亚亚巴特分子动力学 (N2AMD),这是一个使用深度神经哈密尔顿的框架,用于在固体中准确和高效的激发状态动力学模拟. 这种方法增强了半导体载体重组的模拟.

科学领域:

计算材料科学科学计算材料科学
量子化学是一个量子化学.
固态物理固态物理

背景情况:

非adiabatic分子动力学 (NAMD) 对于理解材料中的兴奋状态过程至关重要.
现有的NAMD方法在准确性和效率方面面临挑战,特别是在大规模模拟方面.
已经探索了机器学习方法,但通常间接预测数量.

研究的目的:

为NAMD模拟开发一种新,准确和高效的框架.
使用深度神经汉密尔顿数直接计算关键NAMD量.
为了实现半导体激发状态动态的大规模模拟.

主要方法:

实现N2AMD (神经网络非对应分子动力学) 使用E(3) 相当的深度神经哈密尔顿式.
直接计算NAMD数量,包括非adiabatic合向量.
使用经典路径近似 (CPA) 模拟原始和缺陷半导体中的载体重组的应用.

主要成果:

在混合功能层面上,N2AMD实现了NAMD模拟的高精度和效率.
该框架成功模拟了半导体中的大规模载体重组,性能优于传统方法.
证明了预测非adiabatic合向量的潜力,并超越了CPA.

结论:

N2AMD提供了一种可靠和高效的方法,用于在凝结材料中准确的NAMD模拟.
该框架显示出极好的通用性,并与现有的NAMD技术无集成.
这种方法显著改善了激发状态动态的模拟,特别是半导体中的载体重组.

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