Empirical modeling of dopability in diamond-like semiconductors. (December 2018)

• Record Type: Journal Article
• Authors: Miller, Samuel; Dylla, Maxwell; Anand, Shashwat; Gordiz, Kiarash; Snyder, G.; Toberer, Eric
• Is Part Of: Npj computational materials. Volume 4:issue 1(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-12
• Subjects: Materials science -- Computer simulation -- Periodicals; Materials science -- Mathematical models -- Periodicals; Materials science -- Computer simulation; Electronic journals; Periodicals; 620.110285
• DOI: 10.1038/s41524-018-0123-6
• Languages: English
• ISSNs: 2057-3960

Abstract

Abstract 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 bothp - andn -type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials. THERMOELECTRICS: Looking back is looking forward Experimental carrier concentration can serve as the basis for a model to understand and predict high performance thermoelectrics. Carrier concentration is instrumental in controlling properties. Despite significant experimental progress, establishing guidelines towards the desired performance through doping remains challenging. Now, a team from Northwestern University, Colorado School of Mines, and National Renewable Energy Laboratory in USA have predicted the dopability ranges of several diamond-like semiconductors, based on data from experimentally reported doping limits for 127 compounds. Several materials that combine simultaneously promising thermoelectric quality factor and complementary dopability are singled out. Apart from shedding light on what drives dopability in this family, the model also suggests that a number of less-studied compounds deserve more attention.