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Jablonka Lab (Lab for AI for Materials)

@jablonkagroup.bsky.social
29 followers 3 following 20 posts

Team-run account for the group led by @kjablonka.com

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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 20/05/2025
Training large language models for chemistry is bottlenecked by one critical problem: there is no unified dataset that connects all chemical domains.
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 28/04/2025
We're excited to present our posters today at the AI4Mat workshop at #ICLR25 #AI4Mat #Singapore
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 25/04/2025
LAMA Lab at ICLR in Singapore! #iclr2025 #singapore #AI #ML #chemistry #iclr
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 21/04/2025
we're ready for spring! team building is always more fun when it's outside ☀️
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 18/03/2025
Day 1 of the Foundation Models workshop hosted by the ELLIS Winter School!
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 11/03/2025
🚀ChemBench just leveled up! We’re thrilled to announce the latest release of ChemBench—now smarter and smoother! Dive into benchmarking any chemistry AI model with our revamped framework, designed for flexibility and ease. #ChemistryAI #MachineLearning #OpenScience #Innovation
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 06/03/2025
🚀Our revised MaCBench paper is now on arxiv! arxiv.org/pdf/2411.16955 Key updates! 🌟Robust reproducibility: 5x experiment runs + error bars for statistical confidence 🌟Full dataset & leaderboard: Now live on HuggingFace with model comparisons huggingface.co/spaces/jablo...
Fig A: Bar plot of model performance comparison with error bars
Fig B: Radar plot of relative performance for each model for each subtopicMaCBench leaderboard hosted on HuggingFace spaces
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Jablonka Lab (Lab for AI for Materials) @jablonkagroup.bsky.social · 27/11/2024
Are Vision Language Models ready for scientific research? 🧑‍🔬🧪 We compared leading VLLMs on the three pillars of chemical and material science discovery: data extraction, lab experimentation and data interpretation. arxiv.org/abs/2411.16955
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