SciPost Phys. 8, 087 (2020) ·
published 15 June 2020
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· pdf
Detecting the subtle yet phase defining features in Scanning Tunneling
Microscopy and Spectroscopy 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, demonstrating it is a higher level of complexity than nematic
order detected from Bragg peaks which requires just two neurons. We apply the
finalized ANN to experimental STM data on CaFe2As2, and it predicts nematic
symmetry breaking with 99% confidence (probability 0.99), in agreement with
previous analysis. Our results suggest ANNs could be a useful tool for the
detection of nematic order in STM data and a variety of other forms of symmetry
breaking.
