Electrolyte engineering for CO2 reduction by machine learning force field. This project aims to bridge a critical knowledge gap in applying machine learning force field methods to CO2 reduction for hi

Description

Electrolyte engineering for CO2 reduction by machine learning force field. This project aims to bridge a critical knowledge gap in applying machine learning force field methods to CO2 reduction for high-value C2 products powered by renewable energy. Leveraging the state-of-the-art machine learning force field for electrolyte prediction, this project proposes a novel approach to mitigating greenhouse gas emission, paving a new way for understanding the critical role of electrolyte composition, pH, cation/anion concentration, and electrode potentials at the solid-liquid interface. The outcome of this project is optimized electrolyte towards CO2 reduction to C2 products, significantly reducing greenhouse gas emissions and advancing green chemistry through machine learning-driven innovation in electrocatalysis.. Scheme: Discovery Early Career Researcher Award. Field: 4016 - Materials Engineering. Lead: Dr Xin Mao