Observation of nonFermi liquid physics in a quantum critical metal via quantum loop topography
Abstract
NonFermi liquid physics is a ubiquitous feature in strongly correlated metals, manifesting itself in anomalous transport properties, such as a $T$linear resistivity in experiments. However, its theoretical understanding in terms of microscopic models is lacking despite decades of conceptual work and attempted numerical simulations. Here we demonstrate that a combination of sign problemfree quantum Monte Carlo sampling and quantum loop topography, a physicsinspired machine learning approach, can map out the emergence of nonFermi liquid physics in the vicinity of a quantum critical point with little prior knowledge. Using only three parameter points for training the underlying neural network, we are able to reproducibly identify a stable nonFermi liquid regime tracing the fan of a metallic quantum critical points at the onset of both spindensity wave and nematic order. Our study thereby provides an important proofofprinciple example that new physics can be detected via unbiased machinelearning approaches.
 Publication:

arXiv eprints
 Pub Date:
 July 2020
 arXiv:
 arXiv:2007.07898
 Bibcode:
 2020arXiv200707898G
 Keywords:

 Condensed Matter  Strongly Correlated Electrons
 EPrint:
 6 pages, 4 figures, attached supplementary materials