Empirical modeling of dopability in diamond-like semiconductors

Miller, Samuel A.; Dylla, Maxwell; Anand, Shashwat; Gordiz, Kiarash; Snyder, G. Jeffrey; Toberer, Eric S.

doi:10.1038/s41524-018-0123-6

Title: Empirical modeling of dopability in diamond-like semiconductors

Journal Article · 05 December 2018 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-018-0123-6 · OSTI ID:1489329

^[1]; Dylla, Maxwell ^[1]; Anand, Shashwat ^[1]; Gordiz, Kiarash ^[2];

^[1]; Toberer, Eric S. ^[3]

Northwestern Univ., Evanston, IL (United States)
Colorado School of Mines, Golden, CO (United States)
Colorado School of Mines, Golden, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)

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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1489329

Report Number(s):: NREL/JA-5K00-73016

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 4; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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