4 papers
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…
Latent Spaces for Langevin Dynamics
Andy Bruce, Alexander Aghili, Razvan Marinescu +1
In the field of machine learning coarse-grained potentials in molecular dynamics, many propagators require that the effective Hamiltonian is quadratic in momentum, thus limiting th…
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…