Johann Brehmer
Machine learner & physicist. At CuspAI, I teach machines to discover materials for carbon capture. Previously Qualcomm AI Research, NYU, Heidelberg U.
- Reposted by Johann BrehmerScaling Laws in Particle Physics Data! This is a result I've been itching to share and it's finally out. One of the big open questions is how much better AI-based methods at particle colliders can still become. 1/4
- Happy New Year! Do your plans for 2026 include... - working with a great team lead by @wellingmax.bsky.social and @aronwalsh.github.io? - living in Amsterdam, Berlin, London, or Cambridge? - using fun tools from ML and material science? - solving important problems? Then join us at CuspAI! 1/2
- We're looking for a few profiles: 1. generative models (come work with me!): jobs.ashbyhq.com/cuspai/b8108... 2. molecular simulation: jobs.ashbyhq.com/cuspai/3bb2f... 3. materials foundation models: jobs.ashbyhq.com/cuspai/90a8f... 4. semiconductors: jobs.ashbyhq.com/cuspai/f6a81... 2/2
- Come work with us at @cuspai.bsky.social in the generative model team! Excited about flow / diffusion models and chemistry? Looking for impact? jobs.ashbyhq.com/cuspai/b8108... Join a great team lead by @wellingmax.bsky.social and Aron Walsh, work in Amsterdam / Cambridge / London / Berlin. 1/2
- Or join our semiconductor team! Still plenty of ML (especially generative models) in this role, but with a focus on semiconductor modelling and design. jobs.ashbyhq.com/cuspai/f6a81... Same great team, but working directly w/ Aron Walsh in London. Happy to chat! 2/2
- Today at NeurIPS (SD), come meet the @cuspai.bsky.social team and learn about our work! Find us at 5:30pm at booth 1343 (Renaissance Philanthropy / UK government)
- Reposted by Johann BrehmerAs we go into the Thanksgiving holiday, I wanted to express my thanks to my collaborators @johannbrehmer.bsky.social @glouppe.bsky.social, Juan Pavez, @smsharma.bsky.social. Recently, I was awarded the Pritzker Prize for AI in Science for work on SBI. That wouldn't have never happened without them.
- I'll be at NeurIPS next week – together with my @cuspai.bsky.social colleagues @jonkhler.argmin.xyz, @hannahopenshaw.bsky.social, Friso de Kruiff, and @wellingmax.bsky.social. If you'd like to chat about ML for material discovery, generative models, or start-ups made in Europe, ping me!
- Reposted by Johann BrehmerIt was great to work on the ODAC25 paper with the Meta FAIR Chemistry and Georgia Tech. A leap forwards in modelling direct air carbon capture with metal organic frameworks, with much better data and larger models. Paper: arxiv.org/abs/2508.03162 Data and models: huggingface.co/facebook/ODA...
- Reposted by Johann BrehmerAre you tired of context-switching between coding models in @pytorch.org and paper writing on @overleaf.com? Well, I’ve got the fix for you, Neuralatex! An ML library written in pure Latex! neuralatex.com To appear in Sigbovik (subject to rigorous review process)
- Reposted by Johann BrehmerAn unexpected surprise. The 2025 Breakthrough Prize in Fundamental Physics honors over 13,000 researchers whose labors have led to the precise description the Higgs mechanism, … breakthroughprize.org/News/91 @CERN
- Reposted by Johann BrehmerOn March 7th, we’re Standing Up for Science— and against political censorship, autocracy, and fascism. Science stands at a crossroads. This is a wider fight for truth, for democracy, and for the future. We hope you join us. www.standupforscience2025.org
- Reposted by Johann Brehmer📣 Hiring! I am looking for PhD/postdoc candidates to work on foundation models for science at @ULiege, with a special focus on weather and climate systems. 🌏 Three positions are open around deep learning, physics-informed FMs and inverse problems with FMs.
- Reposted by Johann BrehmerExcellent talk by @johannbrehmer.bsky.social On “Does equivariance matter at scale?” At NeurReps workshop arxiv.org/abs/2410.23179 www.neurreps.org
- Just arrived in Vancouver for #NeurIPS. I'm looking forward to meeting old and new friends, learning a thing or two, and presenting some recent work: 1/6
- On Thursday from 11:00 to 14:00, I'll be cheering on @jonasspinner.bsky.social and Victor Bresó at poster 3911. They built L-GATr 🐊: a transformer that's equivariant to the Lorentz symmetry of special relativity. It performs remarkably well across different tasks in high-energy physics. 2/6
- Combining L-GATr with Riemannian flow matching, they also constructed the first Lorentz-equivariant generative model. arxiv.org/abs/2405.14806 With @jonasspinner.bsky.social, Victor Bresó, @pimdh.bsky.social, Tilman Plehn, and Jesse Thaler. 3/6
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View full threadIf you're in Vancouver and want to chat about these papers, material discovery, or anything else, come by the posters or ping me! 6/6
- Reposted by Johann BrehmerA common question nowadays: Which is better, diffusion or flow matching? 🤔 Our answer: They’re two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. That’s great: It means you can use them interchangeably.
- Reposted by Johann BrehmerThe sbi package is growing into a community project 🌍 To reflect this and the many algorithms, neural nets, and diagnostics that have been added since its initial release, we have written a new software paper 📝 Check it out, and reach out if you want to get involved: arxiv.org/abs/2411.17337
- Reposted by Johann BrehmerThrilled to announce that L-GATr is going to NeurIPS 2024! Plus, there is a new preprint with extended experiments and a more detailed explanation. Code: github.com/heidelberg-h... Physics paper: arxiv.org/abs/2411.00446 CS paper: arxiv.org/abs/2405.14806 1/7
- Reposted by Johann BrehmerMilestone: our review paper “The Frontier of Simulation-Based Inference” coauthored with @glouppe.bsky.social & @johannbrehmer.bsky.social hit 1000 citations. I’m very excited about the potential for these methods to transform science! www.pnas.org/doi/10.1073/... simulation-based-inference.org
- Reposted by Johann BrehmerThe snow is gently falling outside the window, the models are training, what could be better? Two articles cool to read: Does Equivariance matter at scale? (@johannbrehmer.bsky.social et al.) arxiv.org/abs/2410.23179 Denoising Diffusion Bridge Models (Linqi Zhou et al.) arxiv.org/pdf/2309.16948
