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MatterGen is a diffusion-based generative model for inorganic crystal structure generation. Use this skill to understand what MatterGen can do and which sub-skill to load. For generation, fine-tuning, or evaluation tasks, load the appropriate sub-skill.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Generate VASP inputs (INCAR/POSCAR/POTCAR/KPOINTS) via pymatgen.io.vasp.sets for DFT calculations: MPRelaxSet (geometry relaxation), MPStaticSet (SCF), MPNonSCFSet (band structure), MatPESStaticSet (MLFF energy/force labeling). INCAR is driven by pymatgen defaults; the agent only supplies user_incar_settings overrides. Use when the user asks to prepare VASP calculations, run DFT, or generate MLFF training data from structures. Do NOT use for VASP result post-processing / analysis, non-VASP DFT codes, or molecular dynamics — this skill only generates input files and submits jobs.
Deep potential models finetuning and testing using the DeePMD-kit. Use this skill whenever finetuning a Deep Potential (DPA-1 / DPA-2 / DPA-3 / DPA-4) model or running model tests on a dataset. The oldest DP descriptors such as se_e2_a, se_e2_r, and se_e3 are no longer supported. Training from scratch is NEVER advised unless distilling a student model from a teacher model. Multitask fine-tuning is NOT supported.
Skill for running ASE calculations, including energy/force/stress evaluation, molecular dynamics (MD) and structure optimization. Now only use machine-learned force fields (MLFFs) as calculators.
Concept skill for Machine Learning Force Fields (MLFFs). Describes what MLFFs are, the distinction between fine-tuning and distillation, and which tool skills to use. Load this before selecting a specific MLFF framework (DeePMD, MatterSim, etc.).
Concept skill for Molecular Dynamics (MD) simulation. Describes MD ensembles, key parameters, and which tool skills to invoke for MD runs. Use this to understand MD concepts before selecting a specific simulation tool.
Equation-of-State (EOS) benchmark skill — compare DFT, pretrained, and finetuned model E(V) curves to evaluate force-field quality for bulk crystals and simple systems.
| name | mattergen |
| description | MatterGen is a diffusion-based generative model for inorganic crystal structure generation. Use this skill to understand what MatterGen can do and which sub-skill to load. For generation, fine-tuning, or evaluation tasks, load the appropriate sub-skill. |
| metadata | {"dependent_skills":["mattergen-generation","mattergen-finetune","mattergen-evaluation"],"tags":["MatterGen","crystal-generation","inverse-design","generative-model"]} |
MatterGen is a diffusion-based generative model for inorganic crystal structure generation. It can generate novel crystal structures either unconditionally or conditioned on target properties (chemical system, space group, band gap, bulk modulus, magnetic density, etc.).
| Sub-skill | Purpose |
|---|---|
mattergen-generation | Generate crystal structures from a pre-trained or fine-tuned checkpoint |
mattergen-finetune | Fine-tune a MatterGen checkpoint on property-labeled structures (CSV + CIF) |
mattergen-evaluation | Evaluate generated structures against S.U.N. criteria (Stable, Unique, Novel) |
load_skill("mattergen-generation") — generate structures.load_skill("mattergen-evaluation") — filter by S.U.N. criteria.load_skill("mattergen-finetune") — fine-tune on top candidates, then repeat.