Mechanistic Understanding and ML-Guided Material Discovery & Design
for Sustainable Energy and Beyond
Research Overview
Our group focuses on the fundamental understanding, discovery, and design of sustainable materials for current and emerging energy applications, with particular emphasis on electrochemical energy storage for transportation and grid systems. We integrate fundamental materials science, electrochemistry, and interfacial science and engineering with high-throughput experimentation, data science, and machine learning to accelerate materials discovery and development.
An overarching goal of our group is the development of closed-loop, autonomous research platforms for energy materials, in which high-throughput experiments and machine-learning models are tightly coupled to rapidly explore complex materials spaces, uncover governing descriptors, and guide rational materials design. Through this approach, we aim to move beyond trial-and-error experimentation toward predictive, principles-driven design of energy materials.
Research Pillars
- Fundamental electrochemical interfaces & reaction mechanisms
- High-throughput and autonomous experimental platforms
- Machine-learning-guided materials design with a focus on electrolyte and electrode materials
- Sustainable energy storage for transportation and electric grid