4 papers
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…
Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
Sanya Murdeshwar, Sanjit Shashi, Kevin Bachelor +3
Coarse-grained (CG) molecular dynamics enables simulations of atomic systems such as biomolecules at timescales inaccessible to all-atom (AA) methods, but existing CG neural potent…
Holographic generative flows with AdS/CFT
Ehsan Mirafzali, Sanjit Shashi, Sanya Murdeshwar +3
We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/co…
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…