Computational prediction of hot-electron chemistry: Towards electronic control of catalysis

生活水平的提高和世界人口的增长是温室气体排放和工业能源使用持续增加的驱动力。这给化学工业带来了越来越大的压力，要求它们在创新技术的基础上开发更可持续和更有效的化学转化。光驱动催化提供了一种比传统工业规模催化更可持续和节能的化学转化途径，通过用丰富的原料（如来自大气的二氧化碳和来自太阳光的可再生能源）取代石化反应物和能源。此外，光能通过金属纳米颗粒中的激发电子（所谓的“热”电子）的转化，选择性地将能量转移到分子，并实现比传统催化更具体的化学反应，从而可能提高产率并减少不需要的副产物。这种非常规化学形式的基础是光、热电子和反应物分子的复杂耦合，对这一点缺乏了解阻碍了对反应参数（如颗粒大小、形状和最佳曝光）的系统设计和研究。然而，目前还没有模型可以将工业相关的设计参数（如纳米颗粒的形状、大小、光强度和频率）与反应速率和周转频率无缝连接起来。这样的模型只能通过同时考虑光驱动的热电子形成和热电子驱动的化学反应动力学的相互作用来构建。热电子化学的预测理论将支持该技术在化学工业中的应用，这有可能显着减少该行业的碳足迹。该项目的目的是开发和利用计算模拟框架，以理解，预测和设计光敏金属纳米颗粒和表面上的光驱动化学反应。其基本愿景是提供和应用量子理论方法，通过对实验可测量的化学反应速率（作为催化剂设计参数的函数）提供准确且可行的计算预测，填补概念和方法学空白。在第一个资助期内，该研究员和他的团队通过结合电子结构理论、机器学习方法、和非绝热分子动力学方法。这些已被应用于仔细研究与实验伙伴密切合作的氢表面化学和金属催化剂上的反应性散射的机制建议。在第二个资助期内，重点将转向光辅助氢化催化和二氧化碳还原化学的实际应用。其目的是提供一个阶跃变化的光驱动催化的一氧化碳和二氧化碳转化的例子的机械理解，使催化剂材料的合理设计具有广泛的影响，连续光化学和电化学应用在工业中。我们将建立结构-反应性关系和反应速率模型，以提高化学过程工程中二氧化碳后处理的工业可行性。

Higher living standards and a growing world population are the drivers behind continuous increases in greenhouse gas emission and industrial energy use. This provides growing pressure on chemical industries to develop more sustainable and efficient chemical transformations based on innovative new technologies. Light-driven catalysis offers a promising route to more sustainable and energy efficient chemical transformations than conventional industrial-scale catalysis by replacing petrochemical reactants and energy sources with abundant feedstocks such as carbon dioxide from the atmosphere and renewable energy from sun light. In addition, the transformation of light energy via excited electrons in metal nanoparticles, so-called "hot" electrons, selectively transfers energy to molecules and enables more specific chemical reactions than conventional catalysis, potentially increasing yield and decreasing unwanted side products. Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. However, no model currently exists that seamlessly connects industrially relevant design parameters, such as nanoparticle shape, size, light intensity and frequency to reaction rates and turnover frequencies. Such a model can only be constructed by simultaneously accounting for the interplay of light-driven hot-electron formation and hot-electron-driven chemical reaction dynamics. A predictive theory of hot-electron chemistry will support adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint.The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces. The underlying vision is to deliver and apply quantum theoretical methods that fill a conceptual and methodological gap by providing an accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters.During the first funding period, the fellow and his team have developed a highly efficient computational chemistry methodology by combining electronic structure theory, machine learning methodology, and nonadiabatic molecular dynamics methods. These have been applied to scrutinize mechanistic proposals of hydrogen surface chemistry and reactive scattering on metal catalysts in close collaboration with experimental partners. In this second funding period, the focus will be switched to deliver on real-world applications of light-assisted hydrogenation catalysis and carbon dioxide reduction chemistry. The aim is to provide a step-change in mechanistic understanding of light-driven catalysis on the example of carbon monoxide and carbon dioxide transformations to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. We will construct structure-reactivity relations and reaction rate models relevant to improve the industrial viability of carbon dioxide reprocessing in chemical process engineering.