Update README.md

README.md

@@ -49,12 +49,17 @@ 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-blue)](https://github.com/facebookresearch/all-atom-diffusion-transformer/)
-[![Weights](https://img.shields.io/badge/Weights-HuggingFace-blue)](https://huggingface.co/chaitjo/all-atom-diffusion-transformer)
+[![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)
-[![Slides](https://img.shields.io/badge/Slides-chaitjo.com-blue)](https://www.chaitjo.com/publication/joshi-2025-allatom/All_Atom_Diffusion_Transformers_Slides.pdf)
+[![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 (* Joint last author).
+
+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
@@ -77,13 +82,15 @@ Examples of 10,000 sampled crystals and molecules are also available:
 
 ## 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)
 
 ```
-@article{joshi2025allatom,
+@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},
-  journal={arXiv preprint},
+  booktitle={International Conference on Machine Learning},
   year={2025},
 }
-```
+```