Automated Algorithms for Band Gap Analysis from Optical Absorption Spectra

Marcus Schwarting, Kevin Talley, Andriy Zakutayev, Caleb Phillips, Sebastian Siol

Research output: Contribution to journalArticlepeer-review

18 Scopus Citations


As high-throughput combinatorial experimental methods become more common, the technical challenge is shifting from producing materials to dealing with increasingly large datasets. One of the most important metrics to determine suitability of semiconductor materials for various applications is the band gap. This paper discusses automated algorithms for determining band gaps from optical absorption spectra. The algorithms are applied to a database of 34,313 optical absorption spectra, and selected results are compared to published theoretical and experimental band gap data from 16 materials sets. The best algorithm determines the band gaps with an accuracy of 0.37 eV for direct- and 0.93 eV for indirect band gaps for >20,000 spectra.

Original languageAmerican English
Pages (from-to)43-52
Number of pages10
JournalMaterials Discovery
StatePublished - Dec 2017

Bibliographical note

Publisher Copyright:
© 2018

NREL Publication Number

  • NREL/JA-2C00-71502


  • Combinatorial characterization
  • High-throughput experiments
  • Multivariate adaptive regression splines (MARS)
  • Optical spectroscopy


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