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机器学习模型的探索,以确定与形状转换相关的反应坐标

Nawavi Naleem¹, Charlles R A Abreu², Krzysztof Warmuz³

¹Chemistry Program, Science Division, New York University, Abu Dhabi, UAE.

The Journal of chemical physics

|July 17, 2023

概括

这项研究比较了17种机器学习方法,用于识别基本的分子动力学变量. 光梯度增强机在确定形状转换的反应坐标方面表现出色.

科学领域:

计算化学是一种计算化学.
分子动力学模拟的模拟.
机器学习应用程序机器学习应用程序

背景情况:

确定集体变量 (CV) 对于理解分子构造转换和提高模拟准确性至关重要.
当前的方法通常依赖于直觉,缺乏系统方法来识别关键反应坐标 (RCs).
高维的构造空间在确定基本的自由度时,对提交者分析 (CA) 构成挑战.

研究的目的:

系统地比较17个机器学习 (ML) 方案在识别准确的反应坐标 (RCs) 的有效性.
评估ML方法在氨酸二聚和氨酸二聚系统的构造过渡分析.
评估分子动力学ML模型的特征重要性解释方法.

主要方法:

承诺者分析 (CA) 与17个不同的机器学习 (ML) 算法相结合.
在真空中对氨酸二和在隐性溶剂中对氨酸二的测试.
与标准特征重要性相比,对模型可解释性的沙普利增量解释 (SHAP) 分析.

主要成果:

光梯度增强机 (LGBM) 方法在识别准确的RC方面表现出卓越的性能.
对于氨酸二,φ和θ二面体被确定为C7ax到C7eq过渡的关键.
对于sarcosine dipeptoid,发现 ψ 和 ω 二面体对于 cisαD 到 transαD 过渡是最重要的.

结论:

机器学习,特别是LGBM,提供了一种强大的,系统的方法,可以从提交者分析中确定反应坐标.
准确识别关键的分子自由度对于理解和模拟构造变化至关重要.
分析整个动态路径,而不仅仅是终点,对于强有力的识别过渡管理特征至关重要.

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