Collaborative Research: CDS&E: Theory-infused Neural Network (TinNet) for Nonadiabatic Molecular Simulations

光致电子刺激反应在自然界中是普遍存在的，例如光合作用，它是由太阳辐射激发的热电子驱动的。太阳能驱动的反应在改变与可持续能源、环境污染控制以及低碳化学品和燃料制造有关的关键化学反应方面也具有巨大的潜力，特别是在催化的帮助下。为此，该项目利用量子化学、机器学习和超快泵浦-探测实验技术的组合力量，揭示将提高光催化反应效率的见解，使支持清洁能源和更清洁环境的技术得以开发。该项目包括教育和推广活动，例如为K-12学生举办的与STEM相关的活动，以鼓励他们的好奇心、批判性思维，以及利用表面物理、材料化学和数学领域的知识综合线索。此外，这个项目的数据将被整合到大学水平的课程中，为学生提供计算和数据启用的科学和工程基础方面的下一代培训。该项目探索了一种计算和数据科学方法来模拟由电子激发共同驱动的金属表面上的非绝热反应动力学。这项研究为超越萨巴蒂尔火山限制的多相催化体系的合理设计提供了选择键活化的知识。该方法建立在由机器学习(ML)势增强的分子模拟的最新进展的基础上，用于绝热基态的快速采样。然而，在许多吸附-底物体系中，电子跃迁在将能量从激发载流子传递到关键反应中间体的化学键的过程中起着重要的作用，这个过程本质上是非绝热的。为了理解非绝热表面反应，旨在最大化能量转移过程的量子效率和键选择性，将开发一个明确考虑电子-声子相互作用的数据高效和可解释的建模框架。随着ML非绝热分子模拟的发展，有关电子激发纳米粒子的结构/组成，例如等离子体金属，和光刺激的物理特性如何影响键激活的效率和选择性的基本问题可以得到解决。该项目由化学、生物工程、环境和运输过程(CBET)部门的催化计划和化学(CHE)部门的化学理论、模型和计算方法(CTMC)计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Photoinduced electron-stimulated reactions are ubiquitous in nature, as exemplified by photosynthesis, which is driven by hot electrons excited by solar radiation. Solar-driven reactions also hold great potential for transforming critical chemical reactions related to sustainable energy, environmental pollution control, and low-carbon chemicals and fuels manufacturing, particularly when aided by catalysis. To this end, the project harnesses the combined power of quantum chemistry, machine learning, and ultrafast pump-probe experimental techniques to reveal insights that will improve the efficiency of photocatalyzed reactions, enabling the development of technologies that support clean energy and a cleaner environment. The project includes educational and outreach activities, such as STEM-related activities for K-12 students to encourage their curiosity, critical thinking, and synthesis of clues with domain knowledge of surface physics, materials chemistry, and mathematics. Additionally, the data from this project will be integrated into college-level courses that provide next-generation training to students in the fundamentals of computational and data-enabled science and engineering.The project explores a computational and data science approach to modeling nonadiabatic reaction dynamics on metal surfaces co-driven by electronic excitations. The study advances knowledge of selective bond activation for rational design of heterogeneous catalytic systems beyond Sabatier volcano limitations. The approach builds on recent advances in molecular simulations enhanced by machine learning (ML) potentials for rapid sampling of adiabatic ground states. In many adsorbate-substrate systems, however, electronic transitions play an important role in channeling energy from excited charge carriers to chemical bonds of key reaction intermediates, the process of which is nonadiabatic in nature. A data-efficient and interpretable modeling framework with explicit considerations of electron-phonon interactions will be developed for understanding nonadiabatic surface reactions, aiming to maximize quantum efficiency and bond selectivity of energy transfer processes. With the development of ML-enabled nonadiabatic molecular simulations, fundamental questions regarding how the structure/composition of electronically-excited nanoparticles, e.g., plasmonic metals, and physical characteristics of light stimuli that impact the efficiency and selectivity of bond activation can be tackled.The project is co-funded by the Catalysis program in the Chemical, Bioengineering, Environmental and Transport Processes (CBET) Division and the Chemical Theory, Models, and Computational Methods (CTMC) program in the Chemistry (CHE) Division.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.