Liu's research focuses on various areas of computational catalysis with the goal to advance fundamental knowledge for catalyst development and catalytic process innovation. Using first-principles-based methods (i.e. density functional theory) as the computational engine, he focuses on building a cascade of molecular-level modeling system, encompassing mechanistic investigation, catalytic trends based catalyst screening and first-principles-based kinetic modelings. These research efforts will significantly accelerate the trial-and-error catalyst development approach for catalytic process innovation.
Research area: First-principles methods, and molecular simulations for catalysis and novel material synthesis processes.
Our current research focuses on three main areas:
- Density Functional Theory and microkinetic modeling of bifunctional catalyst systems
- Molecular simulations of crystal nucleation and phase transition under extreme conditions
- Using machine learning to develop neural network potentials for catalysis and materials simulations
