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Photoinduced reactive dynamics of complex systems

复杂系统的光致反应动力学

基本信息

批准号：
428777718
负责人：
Dr. Roman Ellerbrock
金额：
--
依托单位：
Department of Chemistry
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2019
资助国家：
德国
起止时间：
2018-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/428777718?language=en
关键词：
Photoinduced reactive dynamics complex systems

项目摘要

Photoinduced reactions are important processes in many chemical and biochemical systems. The detailed understanding of these processes often requires a rigorous quantum dynamical simulation of the nuclei involving numerous nonadiabatic transitions. The aim of this project is the development and application of numerical methods to facilitate a detailed quantum dynamical simulation of the nuclei in photoinduced processes of large systems. A central step for the quantum dynamical simulation of photoinduced reactive processes is the construction of high-dimensional ab initio potential energy surfaces (PESs) describing the couplings between electronic states. The present project focuses on the process driven construction of coupled PESs employing direct dynamics methods and neural network based fitting. The ab initio multiple spawning (AIMS) approach combined with a nonlinear dimensionality reduction method will be used to find and process relevant regions in the nonadiabatic dynamics of the system. Based on the ab initio data, vibronically coupled PESs will be constructed using modern neural network based fitting and other machine learning approaches. Multi-configurational time-dependent Hartree (MCTDH) calculations will be used to study the wavepacket dynamics on the resulting coupled PESs and to compute complex spectra which can not be accurately studied using trajectory based methods.Interesting applications include photoinduced dynamics of ethylene upon pi -> pi * excitation and the ring-opening reaction of pyridine.

光诱导反应是许多化学和生物化学体系中的重要过程。对这些过程的详细理解通常需要对涉及大量非绝热跃迁的原子核进行严格的量子动力学模拟。该项目的目的是发展和应用数值方法，以促进大系统光诱导过程中原子核的详细量子动力学模拟。光诱导反应过程的量子动力学模拟的一个核心步骤是构建描述电子态之间耦合的高维从头算势能面。本项目的重点是过程驱动的建设耦合PES采用直接动力学方法和神经网络为基础的拟合。从头算多产卵（AIMS）的方法结合非线性降维方法将被用来寻找和处理相关区域的非绝热动力学系统。基于从头算数据，将使用基于拟合的现代神经网络和其他机器学习方法构建振动耦合PES。多构型含时Hartree（MCTDH）计算将用于研究耦合PES上的波包动力学和计算复杂的光谱，这些复杂的光谱不能用基于轨道的方法精确地研究，有趣的应用包括乙烯在π-> π * 激发下的光诱导动力学和吡啶的开环反应。