标题： 可解释的人工智能（AI）对强关联电子的分析 显示英文标题

标题： Interpretable Artificial Intelligence (AI) Analysis of Strongly Correlated Electrons

摘要： 人工智能（AI）已成为分析科学数据的一种极其强大的工具。 特别是，基于注意力的架构表现出捕捉复杂相关性的非凡能力，并能提供对潜在、 otherwise 不显眼模式的可解释见解。 这一进展促使将AI技术应用于强关联电子的分析，这在使用传统理论方法时仍然以难以研究而著称。 在此，我们提出了新的AI工作流程，用于分析二维（2D）Hubbard模型在广泛温度和掺杂范围内的快照数据集。 二维Hubbard模型是一个典型的强关联系统，包含多种引人入胜的现象，包括莫特绝缘体、异常金属和高温$T_c$超导性。 我们的AI技术为这些现象背后的复杂量子关联提供了新的视角，并促进了对相应强关联系统的超冷原子模拟的通用全息测量。 显示英文摘要

摘要： Artificial Intelligence (AI) has become an exceptionally powerful tool for analyzing scientific data. In particular, attention-based architectures have demonstrated a remarkable capability to capture complex correlations and to furnish interpretable insights into latent, otherwise inconspicuous patterns. This progress motivates the application of AI techniques to the analysis of strongly correlated electrons, which remain notoriously challenging to study using conventional theoretical approaches. Here, we propose novel AI workflows for analyzing snapshot datasets from tensor-network simulations of the two-dimensional (2D) Hubbard model over a broad range of temperature and doping. The 2D Hubbard model is an archetypal strongly correlated system, hosting diverse intriguing phenomena including Mott insulators, anomalous metals, and high-$T_c$ superconductivity. Our AI techniques yield fresh perspectives on the intricate quantum correlations underpinning these phenomena and facilitate universal omnimetry for ultracold-atom simulations of the corresponding strongly correlated systems.