Memory Dependent Langevin Dynamics to Study Nonadiabatic Dynamics at Metal Surfaces

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Chemical reactions at metal surfaces are the backbone of various industries, from catalysis to electrochemistry and surface science. Yet, these reactions are not as simple as they seem. The dynamics of these reactions are often affected by electronic excitations, making them a puzzle to unravel. In recent years, Langevin dynamics has been used as a key to unlock the secrets of chemical reaction dynamics at metal surfaces. However, the commonly used Markov approximation [1] in Langevin dynamics has been found to be lacking, failing to capture the important physics of these reactions. Recent studies have shown that the Markov approximation does not take into account the effect of memory, which is crucial in describing the dynamics of reactions at metal surfaces.Research objectivesTo uncover the hidden dynamics of chemical reactions at metal surfaces, our goal is to develop fully position- and frequency-dependent Langevin dynamics [4]. This will involve developing memory-dependent friction kernels Langevin integration algorithms, and improving machine learning surrogate models. This approach will enable us to study the dynamics of reactions at metal surfaces in a more realistic and accurate way.MethodologyTo achieve the research objectives, the following methods will be used:Quantum chemistry calculations will be used to map energy landscapes and friction tensors for realistic chemical systems [2, 5]Machine learning will be applied in representation of electronic structure theory to minimize errors that arise from quantum chemistryDevelop memory-dependent friction kernels based on quantum chemistry calculationsCreate position- and frequency-dependent Langevin integration algorithmsThe developed methods will be integrated into recently developed dynamics Julia code [3] and tested using realistic chemical systemsSignificanceThe proposed research has the potential to significantly advance our understanding of chemical reaction dynamics at metal surfaces and provide the electronic dynamics researchers a better simulation tool. The development of fully position- and frequency-dependent Langevin dynamics will provide a more accurate and complete picture of the dynamics of these reactions, which can have important implications in fields as follows:1A better insight into reaction mechanisms: It provides detailed information on the reaction mechanisms and pathways at the atomic and electronic level, which is important forunderstanding and optimizing catalytic processes. Design of new catalysts: It can help in the design of new metal catalysts by predicting the activity and selectivity for specific reactions, and by providing information on thepreferred binding sites and the interactions with the substrate.Reduced experimentation: By simulating the reaction conditions, simulations can help to reduce the need for time-consuming and expensive experimentation, and can providepredictions for conditions that are not accessible in the laboratory.