The electronic band structure and crystal structure are the two complementary identifiers of solid-state materials. To cope with the growing size and scale of photoemission data, we developed a data analytics 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 toward integration with materials science databases.

Full story: Xian et al., Nature Comp. Sci. 3, 101 (2023).