Automated algorithms for band gap analysis from optical absorption spectra. (December 2017)

• Record Type: Journal Article
• Authors: Schwarting, Marcus; Siol, Sebastian; Talley, Kevin; Zakutayev, Andriy; Phillips, Caleb
• Is Part Of: Materials discovery. Volume 10(2017)
• Page Start: 43
• Page End: 52
• Publication Date: 2017-12
• Subjects: High-throughput experiments -- Combinatorial characterization -- Optical spectroscopy -- Multivariate adaptive regression splines (MARS); Materials -- Periodicals; Materials; Electronic journals; Periodicals; 677.4
• DOI: 10.1016/j.md.2018.04.003
• Languages: English
• ISSNs: 2352-9245

Abstract

Abstract: 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.