AIMMD¶

AIMMD (AI for Molecular Mechanism Discovery) is a Python package for AI-enhanced path sampling of rare molecular transitions. Short unbiased simulations are shot from points chosen by a learned committor model, producing a diverse ensemble of reactive trajectories — often many more transition events than plain equilibrium sampling at comparable cost. That ensemble is reweighted to recover free-energy profiles and transition rates, and the learned committor doubles as an interpretable reaction coordinate. Runs scale from a laptop (multiprocessing) to HPC clusters (SLURM srun), driving GROMACS or a built-in toy engine.

Highlights¶

Committor-guided two-way shooting in the reactive region.
Rejection-free path sampling (rfps) and classic TPS acceptance (tps).
On-the-fly training and reweighting: the committor model, adaptive bins, and densities update continuously as data arrives.
Free energies and rates from a single self-consistent path ensemble.
Multi-system / multi-ligand runs with one shared committor network.
Biased dynamics (OPES/PLUMED) with Tiwary-Parrinello rate reweighting.
Graph-neural-network committor models (optional).
Local or HPC execution from the same parameter file.