Xu Weijia, Xu Haoyang, Zhu Meifang, Wen Jin
State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
Phys Chem Chem Phys. 2024 Oct 17;26(40):25994-26003. doi: 10.1039/d4cp01497a.
This study sheds light on the exploration of photoresponsive host-guest systems, highlighting the intricate interplay between confined spaces and photosensitive guest molecules. Conducting nonadiabatic molecular dynamics (NAMD) simulations based on electronic structure calculations for such large systems remains a formidable challenge. By leveraging machine learning (ML) as an accelerator for NAMD simulations, we analytically constructed excited-state potential energy surfaces along relevant collective variables to investigate photoisomerization processes efficiently. Combining the quantum mechanics/molecular mechanics (QM/MM) methodology with ML-based NAMD simulations, we elucidated the reaction pathways and identified the key degrees of freedom as reaction coordinates leading to conical intersections. A machine learning-based nonadiabatic dynamics model has been developed to compare the excited-state dynamics of the guest molecule, benzopyran, in both the gas phase and its behavior within the confined space of cucurbit[5]uril. This comparative analysis was designed to determine the influence of the environment on the photoisomerization rate of the guest molecule. The results underscore the effectiveness of ML models in simulating trajectory evolution in a cost-effective manner. This research offers a practical approach to accelerate NAMD simulations in large-scale systems of photochemical reactions, with potential applications in other host-guest complex systems.
本研究为光响应主客体系统的探索提供了线索，突出了受限空间与光敏客体分子之间复杂的相互作用。基于电子结构计算对如此大的系统进行非绝热分子动力学（NAMD）模拟仍然是一项艰巨的挑战。通过利用机器学习（ML）作为NAMD模拟的加速器，我们沿着相关集体变量解析构建了激发态势能面，以有效研究光异构化过程。将量子力学/分子力学（QM/MM）方法与基于ML的NAMD模拟相结合，我们阐明了反应途径，并确定了导致锥形交叉的关键自由度作为反应坐标。已开发出一种基于机器学习的非绝热动力学模型，用于比较客体分子苯并吡喃在气相中的激发态动力学及其在葫芦[5]脲受限空间内的行为。这种比较分析旨在确定环境对客体分子光异构化速率的影响。结果强调了ML模型在以经济高效的方式模拟轨迹演化方面的有效性。本研究提供了一种实用方法，可加速大规模光化学反应系统中的NAMD模拟，在其他主客体复合系统中具有潜在应用。
