Deep learning enabled simulation of plasmonic photocatalysis

深度学习能够模拟等离子体光催化

• 批准号: EP/X014088/1
• 负责人: Reinhard J. Maurer
• 金额: $ 164.11万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX014088%2F1
• 关键词: Deep; learning; enabled; simulation; plasmonic

Plasmonic photocatalysis offers a promising route to more sustainable and efficient chemical transformations. Metal catalysts can harness light via excitation of electrons which selectively transfer energy to molecules and promote chemical reactions. The result is an increase of reaction selectivity and a decrease of unwanted side products. This unconventional form of chemistry involves intricate coupling of light, electronic excitations, and molecular motion, the details of which are still under intense debate. The theoretical study of plasmonic photocatalysis to predict reaction probabilities as a function of catalyst composition, shape, and light exposure is limited by the computational cost of ab initio molecular dynamics simulations of realistic systems. This project seeks to develop and apply new molecular simulation methods that are both accurate and scalable enough to study light-driven chemical reactions on metal catalysts. The major leap this project will take is to develop deep machine learning (ML) surrogate models of electronic structure, based on message-passing neural networks that provide predictions at a fraction of the computational cost of ab initio calculations. Achieving this will decouple computational cost from prediction accuracy. These ML surrogate models will be combined with nonadiabatic molecular simulation methods and mesoscopic light-matter interaction models to enable the simulation of experimentally measurable reaction probabilities by averaging over thousands of reaction events at various reaction conditions. We will showcase the transformational capabilities of our methodology by simulating plasmonic light-enhancement of hydrogen evolution, and carbon monoxide and carbon dioxide reduction as a function of key design parameters. This project will go beyond the state of the art by transforming our ability to design plasmonic catalyst materials, to scrutinize mechanistic proposals, and to guide experiments for key catalytic reactions.

等离子体光催化为更可持续和有效的化学转化提供了有希望的途径。金属催化剂可以通过对电子的激发来利用光，从而选择性地将能量传递到分子并促进化学反应。结果是反应选择性的提高和不需要的副产物的降低。这种非常规的化学形式涉及光，电子激发和分子运动的复杂耦合，其细节仍在激烈的争论中。血浆光催化的理论研究以预测反应概率作为催化剂组成，形状和光暴露的函数受到实际分子动力学模拟的计算成本的限制。该项目旨在开发和应用新的分子模拟方法，这些方法既精确又可扩展，以便研究金属催化剂上的光驱动化学反应。该项目将采取的重大飞跃是基于消息通知的神经网络开发电子结构的替代模型，这些神经网络以从头开始计算的计算成本的一部分提供预测。实现这一目标将使计算成本从预测准确性中解脱出来。这些ML替代模型将与非绝热的分子模拟方法和介观光相互作用模型结合使用，以通过在各种反应条件下平均成千上万的反应事件来实现实验可测量的反应概率模拟。我们将通过模拟氢进化的等离子光增强以及一氧化碳和二氧化碳减少作为关键设计参数的函数来展示我们方法论的转化能力。该项目将通过改变我们设计等离激催化剂材料，仔细检查机械性建议并指导实验的关键催化反应的能力，超越了最新技术的状态。

项目成果

Probing the role of surface termination in the adsorption of azupyrene on copper

• DOI: 10.1039/d3nr04690g
• 发表时间: 2024-02-26
• 期刊: NANOSCALE
• 影响因子: 6.7
• 作者: Klein，Benedikt P.;Stoodley，Matthew A.;Duncan，David A.
• 通讯作者: Duncan，David A.

Energy transfer during hydrogen atom collisions with surfaces

氢原子与表面碰撞期间的能量转移

• DOI: 10.1016/j.trechm.2023.08.007
• 发表时间: 2023
• 期刊: Trends in Chemistry
• 影响因子: 15.7
• 作者: Hertl N

Ab initio calculation of electron-phonon linewidths and molecular dynamics with electronic friction at metal surfaces with numeric atom-centred orbitals

• DOI: 10.1088/2516-1075/acf3c4
• 发表时间: 2023-09-01
• 期刊: ELECTRONIC STRUCTURE
• 影响因子: 2.6
• 作者: Box, Connor L.;Stark, Wojciech G.;Maurer, Reinhard J.
• 通讯作者: Maurer, Reinhard J.

Assessing Mixed Quantum-Classical Molecular Dynamics Methods for Nonadiabatic Dynamics of Molecules on Metal Surfaces.

• DOI: 10.1021/acs.jpcc.3c03591
• 发表时间: 2023-08-10
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY C
• 影响因子: 3.7
• 作者: Gardner, James;Habershon, Scott;Maurer, Reinhard J.
• 通讯作者: Maurer, Reinhard J.