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Markus J. Buehler

2 papers indexed

arxivcs.AIcond-mat.mes-hallcond-mat.mtrl-scics.CL2026-07-22

Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model

Markus J. Buehler

Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics. Here we show that materials science mechanism information in the open-weight google/gemma-4-E4B-it model has three experimentall…

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arxivcs.AIcond-mat.mtrl-scics.CLcs.LG2026-07-01

Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination

Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal, Markus J. Buehler

Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models often produce fluent but weakly traceable responses to open-ended materials design problems, making…

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