Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.

中文翻译：

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使用人工智能可视化二维Fermi-Hubbard模型中的奇怪金属相关性

物质的高度相关阶段通常以简单的电子模式来描述。到目前为止，这一直是研究用超冷原子实现的费米-哈伯德模型的基础。在这里，我们表明，对于具有微妙甚至未知模式的阶段，人工智能（AI）可以为该范例提供无偏的替代方案。在对单个原子种类的快照进行训练的卷积神经网络的过滤器中，自发出现了长程和短程自旋相关性。在模型的鲜为人知的稀有金属相中，我们发现在局部矩快照上训练的更复杂的网络为非费米液体行为产生了有效的有序参数。