Ryota Tomioka

Code, model and data:

t.co/dNi4h1KFzX

Excited to share the news that MatterGen is published on Nature today.

Since the publication of our preprint, we have bee busy improving our evaluation; we have also shown successful exp synthesis!

Grateful for the team members for their hard work and perseverance, and #MSR colleagues for support!

MatterGen is out in Nature! MatterGen is a SOTA generative model for materials design. We also raise the bar for evaluation by considering compositional disorder and experimentally validating model capabilities. Code is open-source!

www.nature.com/articles/s41...
github.com/microsoft/ma...

Excited to finally announce the publication of MatterGen on Nature. MatterGen represents a new paradigm of materials design with generative AI. We are releasing the code of MatterGen under MIT license. Look forward to seeing how the community will use the tool and build on top of it.

Super excited to share that the MatterGen code is now public on GitHub! github.com/microsoft/ma...

📢 Paper + code release 📃💻

After 2 years of work, I'm excited to announce our newest paper, MatterGen, has been published in Nature!
www.nature.com/articles/s41...

We are also releasing all the training data, model weights, model code, and evaluation code on GitHub!
github.com/microsoft/ma...

Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments. www.microsoft.com/en-us/resear...

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design. While ea...

new preprint on chemical synthesis ML models

- showing how to combine multiple models in a principled way
- modern Transformers + GNN to featurize chemical reaction:
- new insights in where the models shine
+ bonus: find the quirky named reaction!

Feedback welcome!

arxiv.org/abs/2412.05269

Do you mean there are implicit choices made by the community based on empirical success? Similarly funny in ML when people claim “my model cannot overfit because it doesn’t have any parameter”

Cecilia Clementi introduces her talk, "Navigating protein landscapes with machine learned coarse-grained models"

Cecilia Clementi (@cecclementi.bsky.social) kicks off the afternoon session of the ELLIS ML4Molecules Workshop in Berlin!

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science.

www.biorxiv.org/content/10.1...

Scalable emulation of protein equilibrium ensembles with generative deep learning Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental t...

Excited to present what we've been up to the last couple years. Introducing BioEmu, a Biomolecular Emulator of protein dynamics: www.biorxiv.org/content/10.1...

GitHub - microsoft/mattersim: MatterSim: A deep learning atomistic model across elements, temperatures and pressures. MatterSim: A deep learning atomistic model across elements, temperatures and pressures. - microsoft/mattersim

Our latest deep-learning-based simulation engine for inorganic materials properties is open sourced! Looking forward to the responses from the community

GitHub: github.com/microsoft/ma...
Doc: microsoft.github.io/mattersim/
Blog: www.microsoft.com/en-us/resear...
#microsoftresearch #ai4science

🚨Our Machine Learning Force Field Mattersim is now available! 🚨

Check it out here 👇
msft.it/6013oBZLt

The force field is designed to be used on a vast range of temperatures and pressures, try it yourself :)

Feedback and suggestions are very welcome!

Hi Ben, count me in please!