  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.
