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.
@article{arxiv.2109.07617,
title = {Exploring DFT$+U$ parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling},
author = {Pedram Tavadze and Reese Boucher and Guillermo Avendaño-Franco and Keenan X. Kocan and Sobhit Singh and Viviana Dovale-Farelo and Wilfredo Ibarra-Hernández and Matthew B Johnson and David S. Mebane and Aldo H Romero},
journal= {arXiv preprint arXiv:2109.07617},
year = {2021}
}