Platform-agnostic OpenMM Forces
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Updated
Feb 2, 2024 - C++
Platform-agnostic OpenMM Forces
Use Gaussian processes as collective variables in molecular simulations with NAMD. Moved to https://git.sr.ht/~jmbr/colvars-gaussian-processes
Deep learning for collective variables.
Predictive collective variable discovery with deep Bayesian models for atomistic systems.
A package to find collective variables of dynamical systems by training neural networks
Permutationally invariant networks for enhanced sampling (PINES)
Useful Collective Variables for OpenMM
Unified Free Energy Dynamics (UFED) simulations with OpenMM
Using supervised machine learning to build collective variables for accelerated sampling
Python Suite for Advanced General Ensemble Simulations
Software Suite for Advanced General Ensemble Simulations
A unified framework for machine learning collective variables for enhanced sampling simulations
Collective variables library for molecular simulation and analysis programs
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