3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.AI2026
SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?
Kevin Han, Renfei Zhang, Kathy Wei +3
LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule drug design (SMDD) tasks across div…
cs.LG2026
Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
Kevin Han, Haolin Cong, Bowen Deng +1
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. Ho…
cs.DC2025★ 3 cited
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Kevin Han, Bowen Deng, Amir Barati Farimani +1
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid…