在复杂的分子系统中检测动态域和局部波动,通过时间错过的邻居混
Martina Crippa1, Annalisa Cardellini2, Cristina Caruso1
1Department of Applied Science and Technology, Politecnico di Torino, Torino 10129, Italy.
概括
我们介绍了Local Environments and Neighbors Shuffling (LENS),这是一个机器学习框架,可以有效地检测复杂系统中的动态域和局部波动. 透镜分析分子动力学轨迹,揭示全系统的行为和转变.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 机器学习应用 机器学习应用
- 计算化学计算化学
背景情况:
- 复杂系统的行为通常由局部动态重组来支配.
- 在动态合奏中检测这些局部波动和集体过渡是具有挑战性的.
- 现有的方法难以应对分子相互作用和动态组合的复杂性.
研究的目的:
- 开发一种新的机器学习框架,用于识别动态领域和局部波动.
- 为分析复杂系统动态提供一种高效和抽象的方法.
- 描述分子系统的宏观动态,包括相变和共存.
主要方法:
- 当地环境和邻居混 (LENS) 描述符的介绍.
- 随着时间的推移,跟踪分子单位微观环境的变化.
- 来自分子动力学轨迹的LENS时间序列数据的统计分析.
主要成果:
- LENS有效地识别了各种系统中的动态领域和局部波动.
- 该框架描述了相位过渡,相位共存和具有局部波动的系统中的全球动态.
- 在分析类似液体,类似固体和动态多样化的分子系统方面表现出效率.
结论:
- 镜片是一个强大的和多功能机器学习方法,用于分析动态复杂性.
- 该方法适用于各种系统,只要可获得轨迹数据.
- 透镜为探索复杂分子和非分子系统的动态行为提供了有价值的工具.
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