Sample PES by MD

Goal

To generate diverse and representative atomic configurations from a starting structure to augment training data for Machine Learning Interatomic Potentials (MLIPs). This is achieved through MD-based sampling with crystal feature clustering.

Instructions

1. Prepare a Foundation Potential: Select an appropriate MLIP model for sampling.

   • Recommended: M3GNet-PES-MatPES-PBE-2025.2 (MatGL) or MACE-MP-small (MACE) for general inorganic materials.

2. Off-Equilibrium Sampling (MD-Clustering):

   • Use the unified sampling script to run a short MD trajectory and pick representative configurations via K-Means clustering of latent features.

   Using MatGL (CHGNet):

   # Env: matgl-agent
   python .agents/skills/mat-sample-pes-by-md/scripts/run_sampling.py input.cif \
       --model_type matgl --model_name CHGNet-PES-MatPES-PBE-2025.2.10 \
       --total_steps 2000 --temperature 1000 --n_clusters 10 --output_dir sampling_results