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普遍存在，例如光合作用，其由太阳辐射激发的热电子驱动。太阳能驱动的反应也具有巨大的潜力，可以改变与可持续能源、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，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综合力量，揭示将提高光催化反应效率的见解，从而开发支持清洁能源和更清洁环境的技术。该项目包括教育和外展活动，例如针对K-12学生的STEM相关活动，以鼓励他们的好奇心，批判性思维以及将线索与表面物理，材料化学和数学领域知识相结合。此外，该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驱动的科学与工程基础知识的下一代培训。该项目探索了一种计算和数据科学方法，用于模拟电子激发共同驱动的金属表面上的非绝热反应动力学。 这项研究的进步知识的选择性键活化的合理设计的多相催化体系超越Sabatier火山的限制。该方法建立在分子模拟的最新进展的基础上，通过机器学习（ML）潜力来快速采样绝热基态。然而，在许多吸附物-基质系统中，电子跃迁在将能量从激发的电荷载流子引导到关键反应中间体的化学键中起重要作用，该过程本质上是非绝热的。将开发一个数据高效和可解释的建模框架，明确考虑电子-声子相互作用，以了解非绝热表面反应，旨在最大限度地提高能量转移过程的量子效率和键选择性。随着ML使能的非绝热分子模拟的发展，关于电子激发的纳米颗粒的结构/组成的基本问题，例如，等离子体金属，以及影响键激活效率和选择性的光刺激的物理特性。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理论，模型，和计算方法（CTMC）计划在化学（CHE）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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.