README.md

---
license: mit
datasets:
- chaitjo/QM9_ADiT
- chaitjo/MP20_ADiT
- chaitjo/QMOF150_ADiT
- chaitjo/GEOM-DRUGS_ADiT
tags:
- chemistry
- materials
- molecules
- crystals
- diffusion
- transformer
- latent-diffusion
- all-atom
model-index:
- name: ADiT
  results:
  - task:
      type: unconditional-molecule-generation
    dataset:
      name: QM9
      type: QM9
    metrics:
    - name: Validity Rate
      type: Validity Rate
      value: 94.45
    source:
      name: Unconditional Molecule Generation
      url: https://paperswithcode.com/sota/unconditional-molecule-generation-on-qm9
  - task:
      type: unconditional-crystal-generation
    dataset:
      name: MP20
      type: MP20
    metrics:
    - name: Validity Rate
      type: Validity Rate
      value: 91.92
    - name: DFT S.U.N. Rate
      type: DFT S.U.N. Rate
      value: 6
    source:
      name: Unconditional Crystal Generation
      url: https://paperswithcode.com/sota/unconditional-crystal-generation-on-mp20
  - task:
      type: unconditional-molecule-generation
    dataset:
      name: GEOM-DRUGS
      type: GEOM-DRUGS
    metrics:
    - name: Validity Rate
      type: Validity Rate
      value: 95.3
    source:
      name: Unconditional Molecule Generation
      url: https://paperswithcode.com/sota/unconditional-molecule-generation-on-geom
library_name: transformers
---

# All-atom Diffusion Transformers

[![arXiv](https://img.shields.io/badge/PDF-arXiv-blue)](https://www.arxiv.org/abs/2503.03965)
[![Code](https://img.shields.io/badge/Code-GitHub-red)](https://github.com/facebookresearch/all-atom-diffusion-transformer/)
[![Weights](https://img.shields.io/badge/Weights-HuggingFace-yellow)](https://huggingface.co/chaitjo/all-atom-diffusion-transformer)
[![X](https://img.shields.io/badge/X_thread-@chaitjo-blue)](https://x.com/chaitjo/status/1899114667219304525)
[![YouTube](https://img.shields.io/badge/Talk-YouTube-red)](https://www.youtube.com/watch?v=NiY4NLzemnU)
[![Slides](https://img.shields.io/badge/Slides-chaitjo.com-green)](https://www.chaitjo.com/publication/joshi-2025-allatom/All_Atom_Diffusion_Transformers_Slides.pdf)
<a target="_blank" href="https://colab.research.google.com/drive/1wHXsP0SHZ-Lx6Brgg-osuvTFrWw3M7oW?usp=sharing">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


Independent reproduction of the paper [*"All-atom Diffusion Transformers: Unified generative modelling of molecules and materials"*](https://www.arxiv.org/abs/2503.03965), by [Chaitanya K. Joshi](https://www.chaitjo.com/), [Xiang Fu](https://xiangfu.co/), [Yi-Lun Liao](https://www.linkedin.com/in/yilunliao), [Vahe Gharakhanyan](https://gvahe.github.io/), [Benjamin Kurt Miller](https://www.mathben.com/), [Anuroop Sriram*](https://anuroopsriram.com/), and [Zachary W. Ulissi*](https://zulissi.github.io/) from FAIR Chemistry at Meta, published at ICML 2025 (* Joint last author).

All-atom Diffusion Transformers (ADiTs) jointly generate both periodic materials and non-periodic molecular systems using a unified latent diffusion framework:
- An autoencoder maps a unified, all-atom representations of molecules and materials to a shared latent embedding space; and
- A diffusion model is trained to generate new latent embeddings that the autoencoder can decode to sample new molecules or materials.

![](https://raw.githubusercontent.com/facebookresearch/all-atom-diffusion-transformer/refs/heads/main/ADiT.png)

Note that these checkpoints are the result of an independent reproduction of this research by Chaitanya K. Joshi, and may not correspond to the exact models/performance metrics reported in the final manuscript.

These checkpoints can be used to run inference as described in the [README on GitHub](https://github.com/facebookresearch/all-atom-diffusion-transformer/). 

Here is a minimal notebook for loading an ADiT checkpoint and sampling some crystals or molecules:
<a target="_blank" href="https://colab.research.google.com/drive/1wHXsP0SHZ-Lx6Brgg-osuvTFrWw3M7oW?usp=sharing">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

Examples of 10,000 sampled crystals and molecules are also available on HuggingFace:
- [Crystals as CIF files](https://huggingface.co/chaitjo/all-atom-diffusion-transformer/resolve/main/ADiT_crystals_mp20.zip) (MP20)
- [Molecules as PDB files](https://huggingface.co/chaitjo/all-atom-diffusion-transformer/resolve/main/ADiT_molecules_qm9.zip) (QM9)
- [Molecules as PDB files](https://huggingface.co/chaitjo/all-atom-diffusion-transformer/resolve/main/ADiT_molecules_geom.zip) (GEOM-DRUGS)

## Citation

Accepted as a conference paper at ICML 2025.
Also presented as a [Spotlight talk](https://www.youtube.com/watch?v=NiY4NLzemnU) at ICLR 2025 AI for Accelerated Materials Design Workshop.
ArXiv link: [*All-atom Diffusion Transformers: Unified generative modelling of molecules and materials*](https://www.arxiv.org/abs/2503.03965)

```
@inproceedings{joshi2025allatom,
  title={All-atom Diffusion Transformers: Unified generative modelling of molecules and materials},
  author={Chaitanya K. Joshi and Xiang Fu and Yi-Lun Liao and Vahe Gharakhanyan and Benjamin Kurt Miller and Anuroop Sriram and Zachary W. Ulissi},
  booktitle={International Conference on Machine Learning},
  year={2025},
}
```