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cs.LG2026
Active Learning for Machine Learning Driven Molecular Dynamics
Kevin Bachelor, Sanya Murdeshwar, Daniel Sabo +1
Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom…
cs.LG2025
A Standardized Benchmark for Machine-Learned Molecular Dynamics using Weighted Ensemble Sampling
Alexander Aghili, Andy Bruce, Daniel Sabo +5
The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comp…
cs.LG2025
TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamics
Alexander Aghili, Andy Bruce, Daniel Sabo +1
Molecular dynamics (MD) simulations provide atomistic insight into biomolecular systems but are often limited by high computational costs required to access long timescales. Coarse…