arXiv：cs.LG· Hongjian Zhou, Xianfan Nie, Sean Wu, Tarun Patel, Jinge Wu, Andrew Liu, Adam Wei Tsen, David A. Clifton·· 3 小时前AI 评分48
AI Theorist 自主揭示 α-RuCl₃ 中的激子结构
The AI Theorist reveals excitonic structure in $\alpha$-RuCl$_3$
AI 导读
AI Theorist 是一套由 AI 智能体组成的系统，通过假设生成、第一性原理计算与证据驱动迭代，自主发现物理模型。该系统应用于 Kitaev 量子自旋液体候选材料 α-RuCl₃，对光学与光电流观测给出新解释，识别出具有不同光学选择定则和实空间分布的激子态。这是首个 AI 系统自主构建物理模型、解释量子材料中未发表实验观测的演示。
正文
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this is the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material, utilizing first-principles electronic-structure and many-body calculations.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.
| Subjects: | Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci) |
| Cite as: | arXiv:2610.02417 [cs.LG] |
| (or arXiv:2610.02417v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02417 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinge Wu [view email]
[v1]
Thu, 1 Oct 2026 19:40:18 UTC (3,898 KB)
来源：arXiv：cs.LG · arxiv.org