Ryota Tomioka
@ryotat.bsky.social
Researcher at Microsoft Research AI for Science
https://scholar.google.co.uk/citations?user=TxdeO-UAAAAJ&hl=en
https://scholar.google.co.uk/citations?user=TxdeO-UAAAAJ&hl=en
Pinned
Ryota Tomioka
@ryotat.bsky.social
· Jan 16
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...
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!
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!
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!
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!
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...
January 16, 2025 at 9:58 PM
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!
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!
Reposted by Ryota Tomioka
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...
www.nature.com/articles/s41...
github.com/microsoft/ma...
January 16, 2025 at 1:33 PM
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...
www.nature.com/articles/s41...
github.com/microsoft/ma...
Reposted by Ryota Tomioka
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.
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...
January 16, 2025 at 10:10 AM
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.
Reposted by Ryota Tomioka
Super excited to share that the MatterGen code is now public on GitHub! github.com/microsoft/ma...
January 16, 2025 at 10:26 AM
Super excited to share that the MatterGen code is now public on GitHub! github.com/microsoft/ma...
Reposted by Ryota Tomioka
📢 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...
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...
January 16, 2025 at 10:15 AM
📢 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...
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...
Reposted by Ryota Tomioka
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...
January 16, 2025 at 10:07 AM
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...
Reposted by Ryota Tomioka
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
- 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
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...
arxiv.org
December 9, 2024 at 2:19 AM
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
- 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
Reposted by Ryota Tomioka
Cecilia Clementi (@cecclementi.bsky.social) kicks off the afternoon session of the ELLIS ML4Molecules Workshop in Berlin!
December 6, 2024 at 12:03 PM
Cecilia Clementi (@cecclementi.bsky.social) kicks off the afternoon session of the ELLIS ML4Molecules Workshop in Berlin!
Reposted by Ryota Tomioka
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...
www.biorxiv.org/content/10.1...
December 6, 2024 at 8:39 AM
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...
www.biorxiv.org/content/10.1...
Reposted by Ryota Tomioka
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...
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...
www.biorxiv.org
December 6, 2024 at 8:22 AM
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...
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: github.com/microsoft/ma...
Doc: microsoft.github.io/mattersim/
Blog: www.microsoft.com/en-us/resear...
#microsoftresearch #ai4science
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
github.com
December 4, 2024 at 10:06 PM
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: github.com/microsoft/ma...
Doc: microsoft.github.io/mattersim/
Blog: www.microsoft.com/en-us/resear...
#microsoftresearch #ai4science
Reposted by Ryota Tomioka
🚨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!
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!
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
msft.it
December 3, 2024 at 5:11 PM
🚨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!
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!