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Nanoscale electronic inhomogeneity in FeSe$_{0.4}$Te$_{0.6}$ revealed through unsupervised machine learning

We report on an apparent low-energy nanoscale electronic inhomogeneity in FeSe$_{0.4}$Te$_{0.6}$ due to the distribution of selenium and tellurium atoms revealed through unsupervised machine learning. Through an unsupervised clustering algorithm, characteristic spectra of selenium- and tellurium-rich regions are identified. The inhomogeneity linked to these spectra can clearly be traced in the differential conductance and is detected both at energy scales of a few electron volts as well as within a few millielectronvolts of the Fermi energy. By comparison with ARPES, this inhomogeneity can be linked to an electron-like band just above the Fermi energy. It is directly correlated with the local distribution of selenium and tellurium. There is no clear correlation with the magnitude of the superconducting gap, however the height of the coherence peaks shows significant correlation with the intensity with which this band is detected, and hence with the local chemical composition.