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Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

Published

Author(s)

Kamal Choudhary, Qin Zhang, Sugata Chowdhury, Nhan V. Nguyen, Zachary T. Trautt, Marcus W. Newrock, Faical Y. Congo, Andrew C. Reid, Francesca M. Tavazza

Abstract

We perform high-throughput density functional theory (DFT) calculations for optoelectronic properties (electronic bandgap and frequency dependent dielectric function) using the OptB88vdW functional (OPT) and the Tran-Blaha modified Becke Johnson potential (MBJ). This data is distributed publicly through JARVIS-DFT database. We used this data to evaluate the differences between these two formalisms and quantify their accuracy, comparing to experimental data whenever applicable. At present, we have 17,805 OPT and 7,358 MBJ bandgaps and dielectric functions. MBJ is found to predict better bandgaps and dielectric functions than OPT, so it can be used to improve the well-known bandgap problem of DFT in a relatively inexpensive way. The peak positions in dielectric functions obtained with OPT and MBJ are in comparable agreement with experiments. The data is available on our websites http://www.ctcms.nist.gov/~knc6/JVASP.html and https://jarvis.nist.gov .
Citation
Scientific Data

Keywords

DFT, Machine-learning, bandgap, dielectric-function
Created May 8, 2018, Updated January 7, 2020