  We present a Machine Learning approach to solve electronic quantum transport
equations of one-dimensional nanostructures. The transmission coefficients of
disordered systems were computed to provide training and test datasets to the
machine. The system's representation encodes energetic as well as geometrical
information to characterize similarities between disordered configurations,
while the Euclidean norm is used as a measure of similarity. Errors for
out-of-sample predictions systematically decrease with training set size,
enabling the accurate and fast prediction of new transmission coefficients. The
remarkable performance of our model to capture the complexity of interference
phenomena lends further support to its viability in dealing with transport
problems of undulatory nature.
