15 papers
MatBind: A Shared Embedding Space for Multimodal Materials Characterization
Le Yang, Anoop K. Chandran, Jona Ãstreicher +8
Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural la…
Reducing cross-sample prediction churn in scientific machine learning
Gordan Prastalo, Kevin Maik Jablonka
Scientific machine learning reports predictive performance. It does not report whether the same prediction would survive a different draw of training data. Across chemistry ben…
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Harshit Bisht, Vinay Kumar, Kevin Maik Jablonka +2
A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists,…
AI scientists produce results without reasoning scientifically
Martiño RÃos-GarcÃa, Nawaf Alampara, Chandan Gupta +5
Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make…
Clever Materials: When Models Identify Good Materials for the Wrong Reasons
Kevin Maik Jablonka
Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemi…
Beyond Learning on Molecules by Weakly Supervising on Molecules
Gordan Prastalo, Kevin Maik Jablonka
Molecular representations are inherently task-dependent, yet most pre-trained molecular encoders are not. Task conditioning promises representations that reorganize based on task d…