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Trails-MD

Trails-MD is a modular framework for adaptive molecular dynamics campaigns. It runs many short MD walkers, projects saved frames into a fixed or machine-learned collective-variable (CV) space, restarts walkers from informative regions, and repeats the cycle.

Key features

  • Engine-agnostic walkers. OpenMM, GROMACS, and Amber share the same adaptive loop.
  • Fixed or learned sampling spaces. User-defined physical CVs, PCA, TICA, TVAE, and Deep-TICA, swappable at configuration time.
  • Interchangeable spawning policies. Density, Voronoi, local-outlier-factor, and farthest-point selection.
  • Lineage-aware exploration. Every spawned frame stores its parent-child ancestry, so connected transition pathways can be reconstructed from otherwise disjoint exploration stages.
  • Restartable campaigns. Per-iteration checkpoints capture the adaptive model, feature history, sampling state, and walker coordinates.
  • HPC scalability. Run on a multi-GPU workstation or dispatch walkers as SLURM / PBS array jobs (execution.backend).

The adaptive loop

  run short MD walkers (local / SLURM / PBS)
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  extract features / project to CV space
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  train or update the CV if space_mode is a learned
  method (PCA / TICA / TVAE / Deep-TICA)
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  spawn new walkers (density / Voronoi / LOF / FPS)
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  iteration budget reached, or bin occupancy
  plateaued? --- yes ---> stop
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              no
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Sampling continues until either the configured iteration budget is reached or grid/Voronoi bin occupancy plateaus (see Concepts). After a campaign, representative structures can seed longer unbiased production runs for post-hoc Markov State Model (MSM) construction — see MSM & kinetic seeding.

Where to go next