2 citations · 4 across the 3 of their papers we have counts for
3 papers
physics.chem-ph2025★ 1 cited
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…
physics.chem-ph2025★ 2 cited
Learning from the electronic structure of molecules across the periodic table
Manasa Kaniselvan, Benjamin Kurt Miller, Meng Gao +2
Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training…
cs.LG2024★ 1 cited
Understanding active learning of molecular docking and its applications
Jeonghyeon Kim, Juno Nam, Seongok Ryu
With the advancing capabilities of computational methodologies and resources, ultra-large-scale virtual screening via molecular docking has emerged as a prominent strategy for in s…