Skill v1.0.0

version: "1.0.0" name: msresearch-mattergen description: MatterGen workflows for property-conditioned inorganic crystal generation, MatterSim triage, adapter fine-tuning, and Azure AI Foundry hosted deployment. Use when users ask about MatterGen, materials discovery, crystal generation, property-conditioned diffusion (bulk modulus, band gap, magnetic density, HHI), MatterSim evaluation, or Azure AI Foundry materials deployment. license: MIT

Scope

• Property-conditioned inorganic crystal generation and candidate triage
• Local CUDA workflows, hosted Azure AI Foundry inference, adapter-based extension
• Generation, evaluation, and adapter fine-tuning only — DFT-level physics validation is out of scope and should be handed to computational chemistry teams

Prerequisites

• Windows users: run inside a WSL2 distro (Ubuntu recommended) with NVIDIA drivers installed on the Windows host (not inside WSL). The MatterGen/MatterSim prototype was developed and tested on WSL2/Ubuntu; native Windows Python/PowerShell is not supported for the local CUDA path. See Microsoft's WSL install guide and NVIDIA's CUDA-on-WSL guide. The Azure AI Foundry hosted path still requires WSL2 for the local CLI tooling used to call it.
• Python 3.10 (3.11+ breaks torch_cluster wheels) with pip and uv
• CUDA-capable GPU (16 GB VRAM recommended) — or Azure AI Foundry access for the hosted path
• Git LFS installed before cloning the MatterGen repo
• MatterSim installed separately for evaluation (pip install mattersim)

Workflow

1. If users need background on what MatterGen and MatterSim are, start with docs/about-mattergen.md before any setup.
2. Recommended on-ramp: Run docs/prototype.md to launch the local web UI — demo mode works with no Azure setup.
3. For scripted, scaled, or CLI-based use, follow docs/quick-start.md instead (local CUDA + Hydra configs + hosted REST).
4. Pick a scenario from docs/application-patterns.md and define success metrics.
5. If you need custom properties or datasets, follow docs/data-integration.md to prepare data and adapters.
6. Apply docs/performance-guide.md for batching, multi-GPU, and cost tuning once the basic loop works.
7. Review docs/alignment-constitution.md before any external sharing or lab handoff.

Routing

• docs/about-mattergen.md — what MatterGen and MatterSim are, how they work, why they matter
• docs/prototype.md — run the local web UI to play with MatterGen + MatterSim (demo mode requires no Azure)
• docs/quick-start.md — local CLI setup, hello world, hosted Azure AI Foundry path
• docs/application-patterns.md — superhard, magnetic, optoelectronic, supply-chain, lab-loop scenarios
• docs/data-integration.md — datasets, CSV→LMDB preprocessing, custom property adapters
• docs/performance-guide.md — hardware sizing, throughput, fine-tuning, hosted cost
• docs/emergency-fixes.md — Git LFS, CUDA, MatterSim, hosted endpoint errors
• docs/alignment-constitution.md — responsible-use guardrails and oversight

Assets

• assets/sampling_conf/ — Hydra sampling configs (default.yaml, csp.yaml) used by mattergen-generate
• assets/prototype/ — Runnable FastAPI + React web app for interactive MatterGen + MatterSim use; see docs/prototype.md to run it
• assets/paper/mattergen-nature-paper.pdf, assets/paper/mattergen-a-new-paradigm-of-materials-design.pdf — Source papers for offline reference

Reference Links

• GitHub: https://github.com/microsoft/mattergen
• Hugging Face: https://huggingface.co/microsoft/mattergen
• Nature paper: https://www.nature.com/articles/s41586-025-08628-5
• Azure AI Foundry Model: https://ai.azure.com/catalog/models/MatterGen