Rate My Professor Wen Wen

Wen's research centers on data-driven materials modeling, leveraging machine learning, high-performance computing, and atomistic simulations to design novel materials for energy storage, catalysis, and healthcare applications. His key contributions include developing equivariant graph neural networks for predicting material properties such as elasticity tensors and NMR shifts, and frameworks like KLIFF for interatomic potentials. Notable publications are "Chemical reaction networks and opportunities for machine learning" in Nature Computational Science (2023), "Machine learning full NMR tensors with equivariant graph neural networks" in the Journal of Physical Chemistry A (2023), and "KLIFF: A framework to develop physics-based and machine learning interatomic potential" in Computer Physics Communications (2021). He has received the NSF LEAPS-MPS grant ($250,000, 2023-2025) as PI and awards including the UH Alternative Textbook Incentive Award (2023) and Doctoral Dissertation Fellowship (2018). Wen maintains active service as symposium chair for AIChE sessions and reviewer for prestigious journals. He developed open-source tools like MatTen and BonDNet, impacting computational materials science.