发表机构
University of Oxford; University of Waterloo; Stanford University; University College London（牛津大学; 滑铁卢大学; 斯坦福大学; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AI理论家系统，通过假设生成、第一性原理计算和证据驱动优化，自主发展物理模型，成功解释了$\alpha$-RuCl$_3$的光学光谱，识别出不同激子态，展示了AI在材料科学中实现自主理论发现的潜力。
AI 中文摘要
实验仪器和自动化技术的进步产生了越来越丰富的数据集，但将实验观测转化为微观理解仍然是科学发现中的一个瓶颈。为了加速这一过程，我们引入了AI理论家（AI Theorist），这是一个由人工智能（AI）智能体组成的系统，通过假设生成、第一性原理计算和证据驱动的优化来自主发现物理模型。我们将该框架应用于$\alpha$-RuCl$_3$，一种实现Kitaev量子自旋液体的主要候选材料，通过光谱研究其电子结构。AI理论家对光学和光电流观测提出了新的解释，识别出具有对比性光学选择规则和实空间分布的不同激子态。据我们所知，这是首次展示AI系统自主发展物理模型来解释量子材料中先前未发表的实验观测，利用第一性原理电子结构和多体计算。我们的结果为材料科学中的自主理论发现建立了一条途径，其中AI智能体使用第一性原理计算将实验观测转化为物理模型和可测试的预测。
英文摘要
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 $α$-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this is the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material, utilizing first-principles electronic-structure and many-body calculations.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.
