The AI Theorist reveals excitonic structure in $\alpha$-RuCl$_3$

Abstract:Advances in experimental instrumentation and automation generate increasingly rich datasets, but turning experimental observations into microscopic understanding remains a bottleneck in scientific discovery. To accelerate this process, we introduce AI Theorist, a system of artificial intelligence (AI) agents for autonomous discovery of physical models through hypothesis generation, first-principles calculations and evidence-driven refinement. We apply the framework to $\alpha$-RuCl$_3$, a leading candidate material for realizing a Kitaev quantum spin liquid, to investigate its electronic structure through optical spectra. AI Theorist develops a new interpretation of the optical and photocurrent observations, identifying distinct excitonic states with contrasting optical selection rules and real-space distributions. To our knowledge, this is the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material, utilizing first-principles electronic-structure and many-body calculations. Our results establish a route to autonomous theoretical discovery in materials science, in which AI agents use first-principles calculations to turn experimental observations into physical models and testable predictions.

Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)

arXiv:2610.02417 [cs.LG]

(or arXiv:2610.02417v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.02417 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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