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Title: Empirical modeling of dopability in diamond-like semiconductors

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Lastly, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.
Authors:
ORCiD logo [1] ;  [1] ;  [1] ;  [2] ; ORCiD logo [1] ;  [3]
  1. Northwestern Univ., Evanston, IL (United States)
  2. Colorado School of Mines, Golden, CO (United States)
  3. Colorado School of Mines, Golden, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Report Number(s):
NREL/JA-5K00-73016
Journal ID: ISSN 2057-3960
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2057-3960
Publisher:
Nature Publishing Group
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; computational methods; thermoelectrics
OSTI Identifier:
1489329

Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, and Toberer, Eric S.. Empirical modeling of dopability in diamond-like semiconductors. United States: N. p., Web. doi:10.1038/s41524-018-0123-6.
Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, & Toberer, Eric S.. Empirical modeling of dopability in diamond-like semiconductors. United States. doi:10.1038/s41524-018-0123-6.
Miller, Samuel A., Dylla, Maxwell, Anand, Shashwat, Gordiz, Kiarash, Snyder, G. Jeffrey, and Toberer, Eric S.. 2018. "Empirical modeling of dopability in diamond-like semiconductors". United States. doi:10.1038/s41524-018-0123-6. https://www.osti.gov/servlets/purl/1489329.
@article{osti_1489329,
title = {Empirical modeling of dopability in diamond-like semiconductors},
author = {Miller, Samuel A. and Dylla, Maxwell and Anand, Shashwat and Gordiz, Kiarash and Snyder, G. Jeffrey and Toberer, Eric S.},
abstractNote = {Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Lastly, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.},
doi = {10.1038/s41524-018-0123-6},
journal = {npj Computational Materials},
number = 1,
volume = 4,
place = {United States},
year = {2018},
month = {12}
}

Works referenced in this record:

Complex thermoelectric materials
journal, February 2008
  • Snyder, G. Jeffrey; Toberer, Eric S.
  • Nature Materials, Vol. 7, Issue 2, p. 105-114
  • DOI: 10.1038/nmat2090