How do I install the Msresearch Mattergen skill?

Run npx skillmds@latest add microsoft/msresearch-mattergen in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Who published Msresearch Mattergen?

Microsoft (@microsoft) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Msresearch Mattergen by microsoft — Agent Skill

Name: Msresearch Mattergen
Author: Microsoft

Msresearch Mattergen

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.

Microsoft 56a5ce0 151 files · 12.8 MB Updated Sep 17, 2026 2.7k repo stars

File contents

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

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

---name: msresearch-mattergendescription: 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](https://learn.microsoft.com/windows/wsl/install) and [NVIDIA's CUDA-on-WSL guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html). 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`)## Workflow0. If users need background on what MatterGen and MatterSim are, start with `docs/about-mattergen.md` before any setup.1. **Recommended on-ramp:** Run `docs/prototype.md` to launch the local web UI — demo mode works with no Azure setup.2. For scripted, scaled, or CLI-based use, follow `docs/quick-start.md` instead (local CUDA + Hydra configs + hosted REST).3. Pick a scenario from `docs/application-patterns.md` and define success metrics.4. If you need custom properties or datasets, follow `docs/data-integration.md` to prepare data and adapters.5. Apply `docs/performance-guide.md` for batching, multi-GPU, and cost tuning once the basic loop works.6. 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

Frequently asked questions