  Non-Fermi 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 problem-free quantum Monte Carlo sampling and quantum loop topography, a
physics-inspired machine learning approach, can map out the emergence of
non-Fermi 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
non-Fermi liquid regime tracing the fan of a metallic quantum critical points
at the onset of both spin-density wave and nematic order. Our study thereby
provides an important proof-of-principle example that new physics can be
detected via unbiased machine-learning approaches.
