Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach,Journal of Chemical Information and Modeling

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Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-09-11 , DOI: 10.1021/acs.jcim.4c00325
Goran Giudetti ₁ , Madhubani Mukherjee ₁ , Samprita Nandi ₂ , Sraddha Agrawal ₁ , Oleg V. Prezhdo _{1,

2} , Aiichiro Nakano _{2,

3,

4}

Affiliation

Unraveling the reaction pathway of photoinduced reactions poses a great challenge owing to its complexity. Recently, graph theory-based machine learning combined with nonadiabatic molecular dynamics (NAMD) has been applied to obtain the global reaction coordinate of the photoisomerization of azobenzene. However, NAMD simulations are computationally expensive as they require calculating the nonadiabatic coupling vectors at each time step. Here, we showed that ab initio molecular dynamics (AIMD) can be used as an alternative to NAMD by choosing an appropriate initial condition for the simulation. We applied our methodology to determine a plausible global reaction coordinate of retinal photoisomerization, which is essential for human vision. On rank-ordering the internal coordinates, based on the mutual information (MI) between the internal coordinates and the HOMO energy, NAMD and AIMD give a similar trend. Our results demonstrate that our AIMD-based machine learning protocol for retinal is 1.5 times faster than that of NAMD to study reaction coordinates.

中文翻译：

探索视网膜光异构化的全局反应坐标：基于图论的机器学习方法

由于其复杂性，阐明光诱导反应的反应途径提出了巨大的挑战。最近，基于图论的机器学习与非绝热分子动力学（NAMD）相结合已被应用于获得偶氮苯光异构化的全局反应坐标。然而，NAMD 模拟的计算成本很高，因为它们需要在每个时间步计算非绝热耦合向量。在这里，我们表明，通过选择合适的模拟初始条件，从头算分子动力学 (AIMD) 可以用作 NAMD 的替代方案。我们应用我们的方法来确定视网膜光异构化的合理全局反应坐标，这对于人类视觉至关重要。在对内坐标进行排序时，基于内坐标和 HOMO 能量之间的互信息 (MI)，NAMD 和 AIMD 给出了类似的趋势。我们的结果表明，我们基于 AIMD 的视网膜机器学习协议在研究反应坐标方面比 NAMD 快 1.5 倍。

更新日期：2024-09-11

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