Detecting nematic order in STM/STS data with artificial intelligence

Detecting the subtle yet phase defining features in Scanning Tunneling Microscopy and Spectroscopy(STM/STS) data remains an important challenge in quantum materials. We meet the challenge of detecting nematic order from local density of states data with supervised machine learning and artificial neural networks for the difficult scenario without sharp features such as visible lattice Bragg peaks or Friedel oscillation signatures in the Fourier transform spectrum. We train the artificial neural networks to classify simulated data of isotropic and anisotropic two-dimensional metals in the presence of disorder. The supervised machine learning succeeds only with at least one hidden layer in the ANN architecture, suggesting the classification scheme is non-linear. We apply the finalized ANN to experimental STM data on CaFe$_2$As$_2$ and it predicts nematic symmetry breaking with 99\% confidence (probability 0.99), in agreement with previous analysis.