py-MCMD

py-MCMD implements hybrid Monte Carlo/molecular dynamics simulations to perform sampling in multiple ensembles (NPT, GC, and Gibbs ensemble Monte Carlo (GEMC)) and to improve sampling efficiency using the coupled-decoupled configurational-bias MC (CD-CBMC) method.

Key Features:

• Hybrid Simulation Approach: Supports hybrid MC/MD simulations to combine Monte Carlo moves with molecular dynamics for enhanced sampling.
• Ensemble Support: Performs simulations in NPT, GC, and Gibbs ensemble Monte Carlo (GEMC) ensembles (and other ensembles).
• Enhanced Sampling Efficiency: Utilizes the coupled-decoupled configurational-bias MC (CD-CBMC) algorithm to accelerate equilibration and sampling convergence.

Scientific Applications:

• Water dynamics in confined spaces: Enables investigation of water behavior and dynamics in confined or heterogeneous environments.
• Protein-ligand interactions: Facilitates sampling of hydration and ligand exchange processes relevant to protein–ligand binding.
• Phase behavior and thermodynamics: Supports studies of phase transitions and thermodynamic properties in chemical systems using ensemble exchanges.

Methodology:

Uses hybrid MC/MD simulations across NPT, GC, and Gibbs ensemble Monte Carlo (GEMC) ensembles and applies the coupled-decoupled configurational-bias Monte Carlo (CD-CBMC) algorithm.