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探索视网膜光异构的全球反应坐标:基于图形理论的机器学习方法

Goran Giudetti¹, Madhubani Mukherjee¹, Samprita Nandi²

¹Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.

Journal of chemical information and modeling

|September 11, 2024

概括

初始分子动力学 (AIMD) 为研究复杂的光诱导反应提供了一个比非adiabatic分子动力学 (NAMD) 更快的替代方案. 这种新方法有效地确定了反应途径,如视网膜光异构化,这对视力至关重要.

科学领域:

计算化学是一种计算化学.
摄影化学的使用.
机器学习机器学习

背景情况:

光诱导的反应是复杂的,并且很难研究.
非adiabatic分子动力学 (NAMD) 与基于图形理论的机器学习工具相结合,有助于确定反应途径.
由于需要频繁的非adiabatic合矢量计算,NAMD模拟在计算上是密集的.

研究的目的:

调查ab initio分子动力学 (AIMD) 作为NAMD的一个计算效率高的替代方案,用于研究光诱导反应途径.
通过使用AIMD.确定可信的视网膜光异构化全球反应坐标.
为了比较AIMD和NAMD研究反应坐标的效率和结果.

主要方法:

使用ab initio分子动力学 (AIMD) 在适当的初始条件下.
应用基于图形理论的机器学习.
使用HOMO energy.分析内部坐标及其相互信息 (MI).
比较AIMD和NAMD模拟结果.

主要成果:

通过AIMD模拟,可以有效地确定光诱导反应的全球反应坐标.
AIMD和NAMD在基于与HOMO能量相互信息的内部坐标排名时产生了类似的趋势.
基于AIMD的机器学习协议在研究反应坐标方面比NAMD快1.5倍.

结论:

AIMD为NAMD提供了一个计算上可行的和高效的替代方案,用于解开光诱导反应的反应路径.
这种方法适用于关键的生物过程,如视网膜光异构化.
该研究强调了研究反应坐标的计算成本的显著加快.

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