Applying machine learning to materials science. Principal Researcher at @MSFTResearch Views are my own https://xitter.gq/t.co/yJVaUUuIk3
Cambridge
Joined October 2021
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📢 Paper + code release 📃💻
After 2 years of work, I'm excited to announce our newest paper, MatterGen, has been published in Nature!
nature.com/articles/s41586-0…
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. msft.it/6012U8zX8
Claudio Zeni retweeted
Our team at MSR AI for science is looking for a post-doc researcher to help us generate chemically diverse molecular data to train Skala, our deep-learning exchange-correlation functional. Come join us!
#ai4science
Claudio Zeni retweeted
Public preview of BioEmu2! @MSFTResearch AI for Science’s new foundation model for biomolecular structure, dynamics and function. Open weights, MIT license.
github.com/microsoft/bioemu2
Movie: BioEmu2-simulated binding of intrinsically disordered peptide PUMA to MCL-1 receptor. 🧵⬇️
Claudio Zeni retweeted
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules.: msft.it/6012a5Ogy
Claudio Zeni retweeted
Synthesis is a major bottleneck in drug discovery. AI synthesis planning can help propose better routes and make generative molecular design tractable.
With our retroChimera model we have been pushing the boundaries of these models further - out in @Nature today. 1/2
Claudio Zeni retweeted
Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. msft.it/6011aOXe5
Claudio Zeni retweeted
We are hiring a postdoc on very large-scale protein modeling and free energy calculation for protein-ligand and membrane protein systems. Join us at @MSFTResearch to develop the next generation of AI models for biomolecular dynamics and function.
apply.careers.microsoft.com/…
Claudio Zeni retweeted
Aurora 1.5 adds 22 more variables, hourly temporal resolution, and probabilistic ensemble forecasting to the Aurora foundation model, making it more useful for real-world weather, climate, and energy applications. msft.it/6017vInGR
Going to @icmlconf this week, looking for exciting ML / Materials research.
If you want to chat, you can find me at the @MSFTResearch booth on Wednesday at 16:00.
Join us at @MSFTResearch AI for science and work on exciting Materials Design challenges!
This is a 2-year Machine Learning post-doc position within the team.
Location: Cambridge (UK), Berlin (DE), or Amsterdam (NL).
Link in comments below 👇
Our team at @MSFTResearch is looking for a 2y ML post-doc to re-think materials design using AI. This is an exceptional opportunity to join our ambitious mission of solving some of the most pressing challenges that exist today.
#ai4science #materials
⬇️See link below ⬇️
Claudio Zeni retweeted
Our team at @MSFTResearch is looking for a 2y ML post-doc to re-think materials design using AI. This is an exceptional opportunity to join our ambitious mission of solving some of the most pressing challenges that exist today.
#ai4science #materials
⬇️See link below ⬇️
Claudio Zeni retweeted
Postdoc openings on experimental data integration integration into BioEmu-next, focusing on cryo-EM and binding affinities. Join us at @MSFTResearch Cambridge or Berlin. #AI #MachineLearning #Protein #Biology
apply.careers.microsoft.com/…
Claudio Zeni retweeted
Excited to announce major #MatterSim updates!
👩🔬 Experimentally synthesized high thermal conductor identified by MatterSim
⚡️ 3-5x inference speed-up
💪 MatterSim-MT: a new multi-task foundation model for in silico materials characterization
⬇️ Details below (1/6)
Claudio Zeni retweeted
MatterSim is expanding what AI can do for materials science—from faster large-scale simulations to MatterSim-MT, a new multi-task model for simulating properties beyond potential energy surfaces alone. msft.it/6017vPamT
Claudio Zeni retweeted
I'm thrilled that the new #deeplearning #DFT Skala-1.1 functional is out! arxiv.org/abs/2506.14665
Skala-1.1 breaks the longstanding accuracy-to-cost trade-off of Jacob's Ladder.
Amazing collaboration with @paolagorigiorgi and the entire @MSFTResearch AI4Science team!
#CompChem
Claudio Zeni retweeted
The latest release of Skala is out! Accuracy largely improved beyond hybrids at semilocal cost! #compchem
Today we're sharing a major Skala update: new paper and model release. Skala is a deep-learned XC functional for DFT: 2.8 kcal/mol on GMTKN55, wins 32/55 subsets & surpasses SOTA hybrids in accuracy at semi-local cost.
Paper: arxiv.org/abs/2506.14665
Code: github.com/microsoft/skala
Claudio Zeni retweeted
Exciting new update of Skala. This table should speak for itself: Skala is now the leading density functional in main-group chemistry surpassing previous SOTA hybrid functional at a cheaper, semi-local cost.
#compchem #ai4science
Today we're sharing a major Skala update: new paper and model release. Skala is a deep-learned XC functional for DFT: 2.8 kcal/mol on GMTKN55, wins 32/55 subsets & surpasses SOTA hybrids in accuracy at semi-local cost.
Paper: arxiv.org/abs/2506.14665
Code: github.com/microsoft/skala
Claudio Zeni retweeted
Today we're sharing a major Skala update: new paper and model release. Skala is a deep-learned XC functional for DFT: 2.8 kcal/mol on GMTKN55, wins 32/55 subsets & surpasses SOTA hybrids in accuracy at semi-local cost.
Paper: arxiv.org/abs/2506.14665
Code: github.com/microsoft/skala
If you are interested in AI, materials science, and working on hard problems with an amazing team, this is the place for you.
We are significantly expanding to accelerate our ambitious plans for AI-driven materials discovery at @MSFTResearch AI for Science. Looking for a Data Engineer, ML Engineer and Applied Scientist (UK/NL/DE).
⬇️See job postings below ⬇️