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Ryota Tomioka @ryotat.bsky.social · 16/01/2025

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!

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Robert Pinsler @rpinsler.bsky.social · 16/01/2025

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...

github.com

GitHub - microsoft/mattergen: 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...

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 c...

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Reposted by Ryota Tomioka

Tian Xie @xie-tian.bsky.social · 16/01/2025

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.

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Claudio Zeni @claudiozeni.bsky.social · 16/01/2025

📢 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...

github.com

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Microsoft Research @msftresearch.bsky.social · 16/01/2025

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...

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Marwin Segler @marwinsegler.bsky.social · 09/12/2024

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

arxiv.org

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...

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Ryota Tomioka @ryotat.bsky.social · 08/12/2024

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”

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John Chodera @jchodera.bsky.social · 06/12/2024

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

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

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Frank Noe @franknoe.bsky.social · 06/12/2024

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...

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Ryota Tomioka @ryotat.bsky.social · 04/12/2024

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

github.com

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

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Reposted by Ryota Tomioka

Claudio Zeni @claudiozeni.bsky.social · 03/12/2024

🚨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!

msft.it

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

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Ryota Tomioka @ryotat.bsky.social · 25/11/2024

Hi Ben, count me in please!

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