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Non-Fermi liquid physics is a ubiquitous feature in strongly correlatedmetals, manifesting itself in anomalous transport properties, such as a$T$-linear resistivity in experiments. However, its theoretical understandingin terms of microscopic models is lacking despite decades of conceptual workand attempted numerical simulations. Here we demonstrate that a combination ofsign problem-free quantum Monte Carlo sampling and quantum loop topography, aphysics-inspired machine learning approach, can map out the emergence ofnon-Fermi liquid physics in the vicinity of a quantum critical point withlittle prior knowledge. Using only three parameter points for training theunderlying neural network, we are able to reproducibly identify a stablenon-Fermi liquid regime tracing the fan of a metallic quantum critical pointsat the onset of both spin-density wave and nematic order. Our study therebyprovides an important proof-of-principle example that new physics can bedetected via unbiased machine-learning approaches.

Observation of non-Fermi liquid physics in a quantum critical metal via quantum loop topography