从一开始就概括了朗格温方程
Pinchen Xie1, Roberto Car1,2,3, Weinan E4,5
1Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544.
概括
我们开发了ab initio通用朗格温方程 (AIGLE) 以使用机器学习来建模缓慢的材料和分子动态. 这种方法可以实现高效的多尺度建模,准确捕捉低频动态和远红外吸收.
科学领域:
- 计算材料科学科学 计算材料科学
- 理论化学 理论化学
- 统计力学 统计力学
背景情况:
- 在材料和分子中建模慢集体变量 (CVs) 是具有计算挑战性的.
- 现有的方法往往难以捕捉跨多个尺度的动态.
- 基于量子力学的原子模拟是准确的,但在计算上昂贵.
研究的目的:
- 为了引入一种基于机器学习的新方法,ab initio泛化朗格文方程 (AIGLE),用于建模缓慢的CV动态.
- 通过从高准确度量子力学模拟中学习参数来实现高效的多尺度建模.
- 为了证明AIGLE在研究晶体酸中中等尺度过程的能力.
主要方法:
- 使用莫里-兹万齐格形式主义开发了AIGLE,结合了由波动-散流定理所限制的力场,内存内核和噪声发生器.
- 集成的老与深潜力的分子动力学和电子密度的功能理论.
- 应用AIGLE研究场驱动的铁电域墙壁动力学和酸中的粗粒电二极管的格子.
主要成果:
- 成功地模拟了晶体酸中中等尺度动态,包括场驱动的域壁运动和二极管格子动态.
- 该方法将ab initio模拟扩展到传统分子动力学无法访问的基于噪声的系统.
- 证明了比分子动力学更大的数量级的计算效率,同时准确地复制低频动力学和远红外吸收.
结论:
- 为材料和分子中缓慢动态的多尺度建模提供了强大而高效的框架.
- 该方法准确地捕捉了低频率的微观动态,这对于理解远红外吸收等现象至关重要.
- AIGLE为模拟复杂系统开辟了新的途径,传统方法在计算上是不可避免的.
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