CAREER: Multiscale Photodynamics Simulations in Solvated and Crystalline Environments

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。东北大学的洛佩斯正在利用计算技术来发现新的光促进（光化学）反应。光化学反应对几个研究部门很有吸引力，因为它们避免了需要大量加热和昂贵的催化剂来获得复杂的高能分子。光化学反应发生在非常快的时间尺度上，通常小于百万分之一秒，这使得用实验观察中间结构非常具有挑战性。该项目旨在使用计算和机器学习技术来了解这些光化学反应的反应性和选择性。洛佩斯小组的研究将使计算预测在现实的，复杂的化学环境（例如，溶剂和结晶相），以更准确的预测和设计原则。这项工作是在数据科学，有机和物理化学的交叉点，并将支持各级年轻科学家的跨学科培训。洛佩斯博士和他的团队将为美国各地的社区大学生创造“防流行”的夏季研究体验，并与科学和工程多样性联盟合作，以并行推广影响。 Steven A.洛佩斯和他的研究小组计划应用和开发计算和机器学习技术来预测复杂环境中的光化学反应结果、机制和立体选择性（例如，分子固体和溶剂化体系）。与热反应不同，光化学反应的结构-性质关系更加复杂，难以理解。激发态结构信息的普遍缺乏限制了结构-反应性关系，并减缓了高产选择性反应的发现。实验和计算技术不能解决短寿命分子激发态（纳米到飞秒尺度）的动态激发态结构。该项目将通过多构型量子化学计算和机器学习加速的非绝热分子动力学模拟，全面探索气体演化反应的反应性和立体选择性。该研究小组将集中在母体和取代的三唑啉，吡唑啉，和diazirines。本项目旨在解决分子激发态的机制和结构，从而针对结构-反应性关系的知识缺口。第二阶段将评估化学环境对这些反应的激发态和基态成分的作用，这是由开放访问的机器学习代码Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics（PyRAI 2 MD）实现的。预期的机械见解有可能使未来的光响应框架（例如共价有机框架和金属有机框架，COF和MOFs）和分子机器的设计在更长的时间内。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).With support from the Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Steven A. Lopez of Northeastern University is using computational techniques to discover new light-promoted (photochemical) reactions. Photochemical reactions are attractive to several research sectors because they avoid the need for extensive heating and expensive catalysts to access complex, high-energy molecules. Photochemical reactions occur on very fast timescales, typically less than one-millionth of a second, making observation of intermediate structures with experiments very challenging. This project aims to use computational and machine learning techniques to understand the reactivities and selectivities of these photochemical reactions. Research in the Lopez group will enable computational predictions in realistic, complex chemical environments (e.g., solvent and crystalline phase) towardsmore accurate predictions and design principles. This work is at the intersection of data science, organic, and physical chemistry and will support the interdisciplinary training of young scientists at all levels. Dr. Lopez and his team will create "pandemic-proof" Summer Research Experiences for community college students across the United States and engage with the Alliance for Diversity in Science and Engineering to parallelize the outreach impact. Steven A. Lopez and his research group plan to apply and develop computational and machine learning techniques to predict photochemical reaction outcomes, mechanisms, and stereoselectivities in complex environments (e.g., molecular solids and solvated systems). Unlike thermal reactions, structure-property relationships are more complex and difficult to understand for photochemical reactions. The general lack of excited-state structural information has limited structure-reactivity relationships and slowed the discovery of high-yielding, selective reactions. Experimental and computational techniques cannot resolve dynamic excited-state structures of short-lived molecular excited states (nano- to femtosecond scale). This project will enable the comprehensive exploration of the reactivities and stereoselectivities of gas-evolving reactions with multiconfigurational quantum chemical calculations and machine-learning-accelerated non-adiabatic molecular dynamics simulations. The research group will focus on parent and substituted triazolines, pyrazolines, and diazirines. This project aims to resolve the mechanisms and structures of molecular excited states, thusly targeting a knowledge gap towards structure-reactivity relationships. A second phase will evaluate the role of the chemical environment on the excited- and ground-state components of these reactions, enabled by an open-access machine learning code Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD). The anticipated mechanistic insights have the potential to enable future design of light-responsive frameworks (e.g. covalent organic frameworks and metal-organic frameworks, COFs and MOFs) and molecular machines in the longer term.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.