msresearch-mattergen

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

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)

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.

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/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

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

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)

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.

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/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

Documentation