learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Generate inorganic material structures using MatterGen, a diffusion-based generative model.
not rated 158 yesterday A 22 tokens
MIT
158Stars on the repository
134Mods indexed here, across every type
yesterdayLast push, which is what freshness is scored on
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ml-mace-finetune
122learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Fine-tune MACE machine learning interatomic potentials on custom datasets.
not rated 158 yesterday A 21 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Fine-tune MatGL machine learning interatomic potentials on custom datasets.
not rated 158 yesterday A 22 tokens
MIT
ml-mlip-automl
124learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Automate hyperparameter tuning for MLIPs (MACE, MatGL, FairChem) using an LLM-driven search framework.
not rated 158 yesterday A 34 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Benchmark MLIP accuracy against a labeled dataset — compute MAE/RMSE for energy/atom and forces, and generate parity plots.
not rated 158 yesterday A 34 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
GPU-accelerated batched inference for MACE, MatGL (TensorNet/M3GNet/CHGNet), and FairChem MLIPs using NValchemi, enabling parallel static, relax, and MD workflows across multiple structures simultaneously.
not rated 158 yesterday A 59 tokens
MIT
ml-mlip-speed
127learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs).
not rated 158 yesterday A 21 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Train a model to predict custom properties of molecules or periodic materials using pretrained SelfConditionedDenoisingAtoms (SCD) foundation models.
not rated 158 yesterday C 35 tokens
MIT
learningmatter-mit/AtomisticSkills
Skill Claude CodeCodex
Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive properties from crystal or molecular structures.
not rated 158 yesterday A 45 tokens
MIT
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