Python · By learningmatter-mit

Prepares supercells for porous frameworks based on minimum interplanar distance and relaxes them using standard MLIP relaxation tools.

learningmatter-mit/AtomisticSkills

Dock small-molecule ligands into a protein receptor using AutoDock Vina (Python API) and save ranked poses + docking metadata for reproducible virtual screening.

learningmatter-mit/AtomisticSkills

Set up, check or troubleshoot how AtomisticSkills runs on this machine -- creating its Python environments, connecting its MCP servers, choosing uv or a container runtime, and configuring API keys.

learningmatter-mit/AtomisticSkills

Build and run LAMMPS molecular dynamics with isolated MLIP-specific binaries (MACE, MatGL/CHGNet, FairChem) to avoid Python and Torch stack conflicts.

learningmatter-mit/AtomisticSkills

Iteratively optimize expensive black-box objectives — such as materials properties, experimental yields, or simulation outputs — by learning from past evaluations to select the most promising next…

learningmatter-mit/AtomisticSkills

Benchmark MLIP accuracy against a labeled dataset — compute MAE/RMSE for energy/atom and forces, and generate parity plots.

learningmatter-mit/AtomisticSkills