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20222026
most citedCoarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

2 citations · 4 across the 5 of their papers we have counts for

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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…