1 citations · 1 across the 2 of their papers we have counts for
4 papers
Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
Weiliang Luo, Heather J. Kulik
Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Beyond the Training Domain: Robust Generative Transition State Models for Unseen Chemistry
Samir Darouich, Jacob W. Toney, Weiliang Luo +3
Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machin…
Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Mouyang Cheng, Weiliang Luo, Hao Tang +6
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as ox…