Quantum Dynamics and Spectroscopy of Reactive Species in Heterogeneous Environments

斯坦福大学的托马斯·马克兰教授获得了化学系化学理论、模型和计算方法（CTMC）项目的资助，以开发理论和计算方法来处理无序材料中发生的反应性化学过程，例如在电化学界面、纳米结和生物质子通道中发现的反应性化学过程。描述这些过程需要能够准确地捕捉这些无序系统中电子和原子核的量子力学，并将它们与实验方法（如超快光谱学）联系起来，这些方法现在为这些过程的机制提供了新的线索。Markland教授的研究将为化学、生物化学和材料科学界提供新的理论方法和算法，并将在广泛使用的软件包中实现。托马斯Markland教授和他的研究团队正在开发方法，以精确模拟和阐明无序分子系统中电子和原子核的量子力学，并将其与实验方法联系起来，例如超快光谱学。他们通过以下方式实现这一目标：（一）引入精确获得非绝热电子表面和用于溶液中的分子与固体（金属或半导体）界面之间的电子转移过程的电子耦合的方法，（ii.）开发量子动力学方法以处理耦合到分子环境（例如电极表面）的系统，（iii.）设计方案以提高经训练以对反应性缺陷进行建模的机器学习势能表面的准确性，以及（iv.）提供了有效构建从多维光谱获得的信号的理论。这些进展将共同提高我们捕获涉及质子缺陷和电子传输过程的量子动力学和电子结构的能力，这些过程对于阐明尖端实验和设计界面催化和介观传输的更有效材料至关重要。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查进行评估来支持的搜索.

Professor Thomas Markland of Stanford University is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry to develop theoretical and computational methods to treat reactive chemical processes that occur in disordered materials such as those found at electrochemical interfaces, in nanojunctions, and biological proton channels. Describing these processes requires the ability to accurately capture the quantum mechanics of both the electrons and nuclei in these disordered systems and to connect them to experimental approaches, such as ultrafast spectroscopies, that are now providing new hints into the mechanisms underlying these processes. Markland’s research will provide new theoretical methods and algorithms that can be used by the chemistry, biochemistry, and materials science communities and will be implemented in widely used software packages.Professor Thomas Markland and his research team are developing methods to accurately simulate and elucidate the quantum mechanics of both the electrons and nuclei in disordered molecular systems and link them to experimental approaches, such as ultrafast spectroscopies. They are achieving this by: (i.) introducing approaches to accurately obtain diabatic electronic surfaces and electronic couplings for electron transfer processes between molecules in solution and solid (metal or semiconductor) interfaces, (ii.) developing quantum dynamics approaches to treat systems coupled to molecular environments such as electrode surfaces, (iii.) devising schemes to improve the accuracy of machine-learned potential energy surfaces trained to model reactive defects, and (iv.) providing the theory to efficiently construct the signals obtained from multidimensional spectroscopies. Together these advances will improve our ability to capture the quantum dynamics and electronic structure of processes involving proton defect and electron transport that are essential to elucidate cutting edge experiments and design more efficient materials for interfacial catalysis and mesoscopic transport.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.