(4ma) AI-Accelerated Multiscale Kinetics Simulation for Green Electrochemistry

The advancement of green electrochemistry, crucial for decarbonization, energy, and sustainability, is hindered by challenges such as low reaction efficiency and the high costs of electrochemical setups. The intricate nature of the electrode-electrolyte interface, characterized by multiple interwoven factors affecting reaction performance, makes traditional trial-and-test methods inadequate for identifying optimal catalysts and reaction conditions. The emergence of machine learning techniques in recent years offers a significant boost in computational capabilities, facilitating the management of complex systems with multi-dimensional variables. My future research proposes the development of an AI-accelerated multiscale kinetics simulation framework to investigate the interactions between various chemical and physical processes at the electrode-electrolyte interface. The research plan will focus on both methodological development and its application to pressing electrochemical challenges, including enhancing the selectivity of the CO₂ reduction reaction and improving the efficiency of electrochemical hydrogenation for biomass conversion.

My academic journey was initiated with a Ph.D. from the University of Oxford, completed in 2017 under the guidance of Prof. Richard Compton. I then progressed to an Associate Research Fellow role (independent researcher) from 2018 to 2021 at Hefei Institutes of Physical Science, Chinese Academy of Sciences, China. My previous research primarily focused on investigating the impact of reaction environments on electrochemical reactivity and selectivity. Recognizing the need for a more comprehensive framework to explore the structure-property relationship of the catalyst, I joined Prof. Tej Choksi’s research group at Nanyang Technological University, Singapore in 2022. Our collaborative efforts with the industry partner Chiyoda Cooperation, Japan have developed a surrogate machine learning model to enable catalyst screening of bimetallic nanoclusters for large hydrocarbon molecules. My previous and current research experience paves the way for my future research, aimed at developing AI-accelerated multiscale kinetics simulation to address practical challenges in the electrochemical conversion and valorisation of CO₂ and biomass.

Teaching Interests

Based on my interdisciplinary research experiences, I would like to teach courses in chemistry, chemical engineering, and material science, such as physical chemistry, reaction kinetics, catalysis, solid-state physics, etc. I also envision opening new courses on numerical simulation which teaches approaches from matrix operations to machine-learning modeling and electrochemistry which introduces electrochemical theories, techniques, and applications.