4 papers · 1 filter
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…
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…
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…