Understanding chemical reactions at the electrochemical interfaces is challenging due to the multiscale nature of the phenomena, theoretical descriptions of which would require the prediction of adsorbate binding, effects of potential, solvation, kinetics, and etc. In this talk, I will present some of our recent efforts to understand electrochemical reactions such as nitrogen reduction, hydrogen evolution, and carbon dioxide reduction reactions, by combining machine learning and multiscale strategies. We present a simple and versatile representation, applicable to any deep-learning models, to predict the binding energy of small molecules, and apply the model to study the extraordinary mass activity of jagged Pt nanowires toward hydrogen evolution reaction. Effect of potential is demonstrated to be critical to properly understand several electrochemical reactions.

Data-driven multiscale models to understand electrochemical reactions

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.