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Title: High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis

Abstract

High-throughput experimentation provides efficient mapping of composition-property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe2O3, Cu2V2O7, and BiVO4. Here, the applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra.

Authors:
 [1];  [1];  [1]
  1. Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena California 91125, United States
Publication Date:
Research Org.:
California Institute of Technology (CalTech), Pasadena, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1328809
Alternate Identifier(s):
OSTI ID: 1333916
Grant/Contract Number:  
SC000499; SC0004993
Resource Type:
Published Article
Journal Name:
ACS Combinatorial Science
Additional Journal Information:
Journal Name: ACS Combinatorial Science Journal Volume: 18 Journal Issue: 11; Journal ID: ISSN 2156-8952
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; high-throughput screening; combinatorial science; band gap; UV−vis spectroscopy; optical spectroscopy; solar fuels

Citation Formats

Suram, Santosh K., Newhouse, Paul F., and Gregoire, John M. High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis. United States: N. p., 2016. Web. doi:10.1021/acscombsci.6b00053.
Suram, Santosh K., Newhouse, Paul F., & Gregoire, John M. High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis. United States. https://doi.org/10.1021/acscombsci.6b00053
Suram, Santosh K., Newhouse, Paul F., and Gregoire, John M. Thu . "High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis". United States. https://doi.org/10.1021/acscombsci.6b00053.
@article{osti_1328809,
title = {High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis},
author = {Suram, Santosh K. and Newhouse, Paul F. and Gregoire, John M.},
abstractNote = {High-throughput experimentation provides efficient mapping of composition-property relationships, and its implementation for the discovery of optical materials enables advancements in solar energy and other technologies. In a high throughput pipeline, automated data processing algorithms are often required to match experimental throughput, and we present an automated Tauc analysis algorithm for estimating band gap energies from optical spectroscopy data. The algorithm mimics the judgment of an expert scientist, which is demonstrated through its application to a variety of high throughput spectroscopy data, including the identification of indirect or direct band gaps in Fe2O3, Cu2V2O7, and BiVO4. Here, the applicability of the algorithm to estimate a range of band gap energies for various materials is demonstrated by a comparison of direct-allowed band gaps estimated by expert scientists and by automated algorithm for 60 optical spectra.},
doi = {10.1021/acscombsci.6b00053},
journal = {ACS Combinatorial Science},
number = 11,
volume = 18,
place = {United States},
year = {Thu Oct 13 00:00:00 EDT 2016},
month = {Thu Oct 13 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1021/acscombsci.6b00053

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Cited by: 91 works
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