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由神经网络预测的相互作用加速的异型粒子的分子动力学模拟
B Ruşen Argun1, Yu Fu2, Antonia Statt3
1Mechanical Engineering, Grainger College of Engineering, University of Illinois, Urbana-Champaign, Champaign, Illinois 61801, USA.
The Journal of chemical physics
|June 24, 2024
概括
神经网络通过直接预测相互作用,绕过昂贵的珠子对珠子计算,加速对异质粒子的模拟. 这种方法显著加快了复杂形状的分子动力学模拟.
科学领域:
- 计算物理学的计算物理.
- 材料科学是一种材料科学.
- 统计力学就是统计力学.
背景情况:
- 模拟异型粒子通常使用由较小的珠子制成的复合硬体.
- 传统方法需要计算上昂贵的珠间距离计算,限制系统大小和模拟时间.
- 对于非球形形状来说,用于直接相互作用预测的分析函数具有挑战性.
研究的目的:
- 开发一种方法,可以根据其相对配置直接预测刚性物体之间的相互作用能量,力和扭矩.
- 在分子动力学模拟中绕过计算密集的珠间距离计算.
- 为了能够更快,更大规模的模拟系统与异性质粒子.
主要方法:
- 训练神经网络来学习从对配置 (质量中心距离和相对方向) 到相互作用能量的映射.
- 使用神经网络的能量输出梯度来导出力和扭矩.
- 使用神经网络衍生的力和扭矩进行分子动力学模拟.
主要成果:
- 基于神经网络的模拟在数量上与立方体和圆柱体的传统模拟相匹配.
- 结构和动态特性表明新方法与传统方法之间的一致性.
- 与传统方法相比,在分子动力学模拟中实现了高达23倍的速度.
结论:
- 神经网络可以有效地预测体间的相互作用,显著加速模拟.
- 该方法适用于各种形状,包括不规则的形状,只要提供足够的训练数据.
- 这种方法为模拟复杂粒子系统提供了一个计算效率高的替代方案.
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