Quickstart

Install

conda env create -f env.yml
conda activate trails-md
pip install -e ".[deep-tica]"     # optional deep-TICA / deep-LDA backends

For a lightweight environment (CV registry and tests without the heavy MD backends):

pip install numpy scipy scikit-learn pydantic pyyaml deeptime pytest torch

Validate a configuration

--check runs all preflight checks (files, executables, settings) and exits without launching MD:

trails-md --config examples/AlaD/config.yaml --check

Run

# Fixed phi/psi CVs, density spawning, 20 iterations
trails-md --config examples/AlaD/config.yaml --iterations 20

# Learned TICA CV on AIB9
trails-md --config examples/AIB9/config_target.yaml --iterations 50

Resume

Every iteration is checkpointed. Resume from the latest (or a specific) one:

trails-md --config examples/AIB9/config_target.yaml --resume --iterations 50
trails-md --config examples/AIB9/config_target.yaml --resume 12 --iterations 50

Inspect results

# Per-iteration coverage / timing log
trails-md-log --run-dir runs/aib9_target

# Reconstruct a connected path between two CV points
trails-md-path \
  --run-dir runs/alad_phi_psi_density \
  --topology examples/AlaD/start.gro \
  --start=-1.05,-0.70 --end=1.05,0.70 \
  --output alad_path.xtc

Each iter_*/ directory holds the trajectories, cvs.npz, and optional features.npz. output.log is a tab-separated per-iteration record.