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Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
| Date | Stars |
|---|---|
| 2026-07-31 | 1780 |
| 2026-08-01 | 1780 |
| 2026-08-06 | 1780 |
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<h1>
<p align="center">
<img src="assets/MatterGenlogo_.png" alt="MatterGen logo" width="600"/>
</p>
</h1>
<h4 align="center">
[](https://www.nature.com/articles/s41586-025-08628-5)
[](https://arxiv.org/abs/2312.03687)
[](https://python.org/downloads)
</h4>
MatterGen is a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
## Table of Contents
- [Installation](#installation)
- [Get started with a pre-trained model](#get-started-with-a-pre-trained-model)
- [Generating materials](#generating-materials)
- [Evaluation](#evaluation)
- [Train MatterGen yourself](#train-mattergen-yourself)
- [Data release](#data-release)
- [Citation](#citation)
- [Trademarks](#trademarks)
- [Responsible AI Transparency Documentation](#responsible-ai-transparency-documentation)
- [Get in touch](#get-in-touch)
## Installation
The easiest way to install prerequisites is via [uv](https://docs.astral.sh/uv/), a fast Python package and project manager.
The MatterGen environment can be installed via the following command (assumes you are running Linux and have a CUDA GPU):
```bash
pip install uv
uv venv .venv --python 3.10
source .venv/bin/activate
uv pip install -e .
```
Note that our datasets and model checkpoints are provided inside this repo via [Git Large File Storage (LFS)](https://git-lfs.com/).
To find out whether LFS is installed on your machine, run
```bash
git lfs --version
```
If this prints some version like `git-lfs/3.0.2 (GitHub; linux amd64; go 1.18.1)`, you can skip the following step.
### Install Git LFS
If Git LFS was not installed before you cloned this repo, you can install it via:
```bash
sudo apt install git-lfs
git lfs install
```
### Apple Silicon
> [!WARNING]
> Running MatterGen on Apple Silicon is **experimental**. Use at your own risk.
> Further, you need to run `export PYTORCH_ENABLE_MPS_FALLBACK=1` before any training or generation run.
## Get started with a pre-trained model
We provide checkpoints of an unconditional base version of MatterGen as well as fine-tuned models for these properties:
* `mattergen_base`: unconditional base model trained on Alex-MP-20
* `mp_20_base`: unconditional base model trained on MP-20
* `chemical_system`: fine-tuned model conditioned on chemical system
* `space_group`: fine-tuned model conditioned on space group
* `dft_mag_density`: fine-tuned model conditioned on magnetic density from DFT
* `dft_band_gap`: fine-tuned model conditioned on band gap from DFT
* `ml_bulk_modulus`: fine-tuned model conditioned on bulk modulus from ML predictor
* `dft_mag_density_hhi_score`: fine-tuned model jointly conditioned on magnetic density from DFT and HHI score
* `chemical_system_energy_above_hull`: fine-tuned model jointly conditioned on chemical system and energy above hull from DFT
The checkpoints are located at `checkpoints/<model_name>` and are also available on [Hugging Face](https://huggingface.co/microsoft/mattergen). By default, they are downloaded from Huggingface when requested. You can also manually download them from Git LFS via
```bash
git lfs pull -I checkpoints/<model_name> --exclude=""
```
> [!NOTE]
> The checkpoints provided were re-trained using this repository, i.e., are not identical to the ones used in the paper. Hence, results may slightly deviate from those in the publication.
## Generating materials
### Unconditional generation
To sample from the pre-trained base model, run the following command.
```bash
export MODEL_NAME=mattergen_base
export RESULTS_PATH=results/ # Samples will be written to this directory
# generate batch_size * num_batches samples
mattergen-generate $REExcerpt of 26,422 characters
Read on GitHub
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Hannes Schulz · @Microsoft · Germany
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Emmanuel Ferdman · Israel
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Ikko Eltociear Ashimine · Japan
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@voxel51
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:be7b251a6bc3011a, llm:Repository topics: generative-ai, materials-design, materials-science. Description: 'Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.'
matched fp:be7b251a6bc3011a, llm:Repository topics: generative-ai, materials-design, materials-science. Description: 'Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.'
matched fp:be7b251a6bc3011a, llm:Repository topics: generative-ai, materials-design, materials-science. Description: 'Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.'