  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.
