  Electronic band structure (BS) and crystal structure are the two
complementary identifiers of solid state materials. While convenient
instruments and reconstruction algorithms have made large, empirical, crystal
structure databases possible, extracting quasiparticle dispersion (closely
related to BS) from photoemission band mapping data is currently limited by the
available computational methods. To cope with the growing size and scale of
photoemission data, we develop a pipeline including probabilistic machine
learning and the associated data processing, optimization and evaluation
methods for band structure reconstruction, leveraging theoretical calculations.
The pipeline reconstructs all 14 valence bands of a semiconductor and shows
excellent performance on benchmarks and other materials datasets. The
reconstruction uncovers previously inaccessible momentum-space structural
information on both global and local scales, while realizing a path towards
integration with materials science databases. Our approach illustrates the
potential of combining machine learning and domain knowledge for scalable
feature extraction in multidimensional data.
