使用局部描述器机器学习打破凝聚物质系统中非adiabatic分子动态的尺寸限制
Dongyu Liu1, Bipeng Wang2, Yifan Wu3
1School of Electronic Engineering, HSE University, Moscow Institute of Electronics and Mathematics (MIEM), Moscow 123458, Russia.
概括
我们开发了一种机器学习方法来加速非adiabatic分子动力学 (NA-MD) 模拟. 这种方法使得能源和光电子材料的大规模,长时间的建模,克服了以前的计算限制.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学是一种材料科学.
- 计算化学是一种计算化学.
背景情况:
- 非相应分子动力学 (NA-MD) 对于理解光化学反应和能量材料中的电荷传输至关重要.
- 目前的NA-MD方法在计算上昂贵,将模拟限制在小型系统和短时间范围内.
- 开发下一代光电子材料需要高效的模拟工具,用于凝结相.
研究的目的:
- 为了克服传统NA-MD模拟的计算局限性.
- 开发一种完全基于机器学习 (ML) 的方法来计算NA-MD属性.
- 为了实现大规模和长时间的NA-MD模拟凝聚物质系统.
主要方法:
- 开发了一种完全机器学习 (ML) 方法,使用基于局部描述符的神经网络.
- 集成的ML模型与密度函数理论 (DFT) 和 NA-MD的经典路径近似 (CPA).
- 在小型系统上训练了ML模型,并将其应用于大规模,长时间的模拟.
主要成果:
- 在NA-MD模拟能力 (系统大小和时间表) 中实现了数量级扩展.
- 通过研究在不同缺陷度的二硫化 (MoS) 中的电荷捕获和再组合来证明这种方法.
- 揭示了电荷动态对缺陷度,温度和载体类型的复杂依赖.
结论:
- 基于ML的NA-MD方法显著提高了计算效率和适用性.
- 这种方法弥合了材料研究理论模型和现实的实验条件之间的差距.
- 在许多纳秒内在千原子系统上实现NA-MD模拟,促进先进材料的设计.
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