1. Exploring DFT$+U$ parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
- Author
-
Tavadze, Pedram, Boucher, Reese, Avendaño-Franco, Guillermo, Kocan, Keenan X., Singh, Sobhit, Dovale-Farelo, Viviana, Ibarra-Hernández, Wilfredo, Johnson, Matthew B, Mebane, David S., and Romero, Aldo H
- Subjects
Condensed Matter - Materials Science ,Condensed Matter - Strongly Correlated Electrons - Abstract
Density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard corrections to treat strongly correlated electronic states. Unfortunately, the exact values of the Hubbard $U$ and $J$ parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the $U$ and $J$ parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest $U$ correction. PBE has the smallest standard deviation and its $U$ and $J$ parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.
- Published
- 2021