论文标题
部分可观测时空混沌系统的无模型预测
Machine learning assisted determination of electronic correlations from magnetic resonance
论文作者
论文摘要
在存在强电子自旋相关性的情况下，超精细的相互作用赋予核自旋之间的远距离耦合。通过磁反应提取有关电子相关性的复杂信息的有效方案尚不清楚。在这里，我们研究机器学习如何提取材料参数并帮助解释磁反应实验。通过无监督的学习发现了对总相互作用强度进行分类的低维表示。监督学习产生的模型可以预测电子相关性的空间程度和总相互作用强度。我们的工作证明了人工智能在新的量子系统探针开发中的实用性，并应用于强相关材料的实验研究。
In the presence of strong electronic spin correlations, the hyperfine interaction imparts long-range coupling between nuclear spins. Efficient protocols for the extraction of such complex information about electron correlations via magnetic response are not well known. Here, we study how machine learning can extract material parameters and help interpret magnetic response experiments. A low-dimensional representation that classifies the total interaction strength is discovered by unsupervised learning. Supervised learning generates models that predict the spatial extent of electronic correlations and the total interaction strength. Our work demonstrates the utility of artificial intelligence in the development of new probes of quantum systems, with applications to experimental studies of strongly correlated materials.
