Processes occurring at electrochemical interfaces are critical to the functionality of most energy storage and conversion devices. However, accessing their full chemical and structural complexity under operating conditions remains a significant challenge, largely due to the highly heterogenous nature of these interfaces. In this talk, I will outline some key difficulties associated with predictive modeling of electrochemical interfaces and review our recent efforts in coupling <i>ab initio</i> molecular dynamics simulations with advanced spectroscopic measurements and machine learning techniques to understand interfacial chemistry and structure. Drawing on our recent studies on hydrogen production, electrical double layer supercapacitor, and materials corrosion, I will show how multiscale modeling and experimental characterization can operate hand-in-hand, with experiments aiding the validation and refinement of interface models, and theory aiding the interpretation of experimental measurements. The examples will specifically highlight the importance of accounting for effects of solvation and applied potentials, as well as structural and chemical changes induced by the presence of environments under electrochemical conditions. The role of machine learning on accelerating theory-experiment and model integrations will also be discussed.

Toward a realistic description of electrochemical interfaces from <i>ab-initio</i> simulations

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ACS Fall 2022

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Toward a realistic description of electrochemical interfaces from ab-initio simulations