ES22-Dabo

Author: Dabo, Ismaila, Materials Science and Engineering, Pennsylvania State University

Title: Extensive benchmarking of DFT+U for band-gap predictions and materials discovery

Abstract: Accurate predictions of band gaps are of practical interest to the modeling and development of semiconductor materials, such as photocatalysts and transparent conductors. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard U correction (DFT+U) applied to band edge states is a computationally tractable approach to improve the accuracy of band structure predictions beyond that of DFT calculations based on (semi)local functionals [1]. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+U can be interpreted as an approximate spectral-potential method when U is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+U to predict band gaps [2]. Here, we present a systematic assessment of DFT+U band gaps computed using self-consistent ab-initio U parameters obtained from density-functional perturbation theory to impose the piecewise linearity of the total energy [3]. Applications of DFT+U as an reliable and efficient method for data-intensive materials discovery will also be discussed [4].

References
[1] Cococcioni M. and de Gironcoli S., Linear response approach to the calculation of the effective interaction parameters in the LDA+ U method, Physical Review B 71, 035105 (2005). DOI: 10.1103/PhysRevB.71.035105
[2] Timrov I., Marzari N. and Cococcioni M., Hubbard parameters from density-functional perturbation theory, Physical Review B 98, 085127 (2018). DOI: 10.1103/PhysRevB.98.085127
[3] Kirchner-Hall N. E., Zhao W., Xiong Y., Timrov I., Dabo I., Extensive benchmarking of DFT+ calculations for predicting band gaps, Applied Sciences 11, 2395 (2021). DOI: 10.3390/app11052395
[4] Xiong Y., Campbell Q., Fanghanel J., Badding C. K., Wang H., Kirchner-Hall N. E., Theibault M. J., Timrov I., Mondschein J. S., Seth K., Katz R., Molina Villarino A., Pamuk B., Penrod M. E., Khan M. M.,  Rivera T., Smith N. C., Quintana X., Orbe P., Fennie C. J., Asem-Hiablie S., Young J. L., Deutsch T. G., Cococcioni M., Gopalan V., Abru√±a H. D., Schaak R. E., Dabo, I., Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen, Energy & Environmental Science 14, 2335-2348 (2021). DOI:  10.1039/D0EE02984J