Dr. Yang has been deeply involved in the theoretical exploration of computational chemistry, with a particular emphasis on molecular dynamics (MD) simulations. He has published over twenty academic papers in journals such asJ. Chem. Theory Comput., J. Physical Chem. Lett.andPhys. Rev. Lett.

Representative achievements:

1) Development and Application of Enhanced Sampling: He developed the MetaITS multi-scale enhanced sampling method, which has shown an improvement in sampling efficiency of more than an order of magnitude for specific systems. This method achieved the first reversible ice-water phase transition process in all-atom MD simulations.

2) Deep Reinforcement Learning in Molecular Modelling and Simulation: Dr Yang and collaborators pioneered the introduction of deep reinforcement learning into MD simulations, leading to the development of a series of innovative algorithms.

3) Development of Molecular Dynamics Simulation Software: In collaboration with Huawei Technologies Co., Ltd., Dr Yang’s team developed an AI-based molecular dynamics simulation software, MindSPONGE. He proposed a novel “AI-like” programming architecture for next-generation MD simulation software, and the article (J. Chem. Theory Comput. 2023, 19, 4338-4350) was selected as “Editor’s Choice” and became one of JCTC’s “Most Read Articles” for last 12 months.