Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence
Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence
复制标题
DOI：
10.1103/physreva.102.033326
复制
发表时间：
2020-09-17
影响因子：
2.9
通讯作者：
Scalettar, Richard T.
中科院分区：
文献类型：
--
作者：
Khatami, Ehsan;Guardado-Sanchez, Elmer;Scalettar, Richard T.
物质的强相关相经常用简单的电子模式来描述。到目前为止，这是研究超冷原子实现的费米-哈伯德模型的基础。在这里，我们证明了人工智能（AI）可以为具有微妙甚至未知模式的阶段提供这种范式的无偏见替代方案。长距离和短距离自旋相关性自发地出现在卷积神经网络的过滤器中，该网络是在单个原子物种的快照上训练的。在不太好理解的奇怪的金属相的模型，我们发现，一个更复杂的网络训练的快照的局部时刻产生一个有效的序参量的非费米液体的行为。我们的技术可用于表征其他相位特有的相关性，在投影测量中没有明显的序参数或签名，并且对通过AI进行强相关系统之外的科学发现具有影响。
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.