Biography:

The laboratory focuses on the development of multi-scale mathematical models for rational design and performance maintenance of advanced catalytic materials. Key discoveries include a volcano-like relationship for oxygen evolution activity in single-atom catalysts, a data-driven method for describing catalyst deactivation, and a Sabatier principle for catalyst-support interactions, laying the foundation for designing long-lasting, high-temperature resistant energy catalysts. Key results are published in Science, Nat. Catal., and Nat. Commun.

Research Interest:

Developing physics-inspired AI methods combined with neural network potential molecular simulations to reveal general scaling and evolutionary laws across systems from electrons to atoms to composite materials.

What You Can Expect in the Project:

Learn to apply physical principles, analytical mathematics, and numerical simulations to develop mathematical models that uncover the laws and hidden order in complex microscale material dynamics, aiming to guide practical processes.

Desired skill and background:

Strong interest in developing mathematical models and aim to focus on theoretical research in multiphase catalysis.