872 citations · 1.1k across the 13 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
physics.chem-ph2026
Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen +4
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained m…
physics.chem-ph2026★ 1 cited
Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
Patrick G. Sahrmann, Benjamin T. Nebgen, Kipton Barros +1
Machine-learned (ML) coarse-grained (CG) models are a promising tool for significantly enhancing the efficiency of molecular simulations by systematically removing degrees of freed…