microsoft/mattergen

Health v2 · maintenance only

Adoption not part of the score

1801 stars · 344 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MatterGen is Microsoft's official implementation of a generative diffusion model for designing inorganic crystalline materials across the periodic table. It ships pre-trained checkpoints and fine-tuned variants that steer generation toward property constraints such as band gap, magnetic density, bulk modulus, chemical system, and space group.

Use cases

• generate novel inorganic crystal structures with a target band gap
• design materials with prescribed bulk modulus or magnetic properties
• fine-tune a generative materials model on custom property constraints
• sample candidate crystal structures for a given chemical system or space group
• evaluate generative models for materials discovery
• train a materials generative model from scratch on MP-20 data

When to choose

• you need property-conditioned generation of inorganic crystal structures
• you want a research-grade, peer-reviewed model with pre-trained checkpoints
• you have a CUDA GPU and want to fine-tune on your own materials datasets

When to avoid

• you need organic or molecular (not crystalline inorganic) materials generation
• you have no GPU access and need fast, reliable inference (Apple Silicon support is experimental)
• you want a production application rather than a research codebase

Facets

library · maturity active

machine-learning deep-learning llm-training data-science simulation machine-learning chemistry artificial-intelligence python generative-model diffusion materials-design crystal-structure-generation pytorch property-conditioned-generation research-code materials-science science linux gpu macos

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For agents

markdown · JSON · MCP: product_card(name="microsoft/mattergen")