ml-property-predictor
OfficialTrain property heads on pretrained MLIPs
Education & Research#train#mace#property prediction#mlip#crystal structures#matgl#intensive extensive
Authorlearningmatter-mit
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill trains a custom property predictor by reusing pretrained MLIP GNN representations, so you can learn new scalar targets (e.g., bulk modulus, bandgap, energies) without building a full model from scratch.
Core Features & Use Cases
- Trainable readout head on top of an MLIP backbone (MACE or MatGL/M3GNet) for custom scalar regression targets.
- Supports intensive vs extensive targets, including practical handling differences between MACE and MatGL for scaling and readout behavior.
- Script-driven workflow that converts structure datasets (.json with Pymatgen dicts or .xyz/.extxyz with ASE/Pymatgen-compatible metadata) into the training format expected by each ML framework.
Quick Start
Ask your agent to train an intensive bulk modulus head using MACE from a JSON dataset of structures and labels by running the MACE training script with the dataset path, target property key, property type, and output directory.
Dependency Matrix
Required Modules
pymatgenasetorchnumpymatgldgltqdmdgl
Components
scripts
💻 Claude Code Installation
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Please help me install this Skill: Name: ml-property-predictor Download link: https://github.com/learningmatter-mit/AtomisticSkills/archive/main.zip#ml-property-predictor Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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