Publication date: Nov 03, 2021
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.
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|3.7 MiB||Compressed file contains 4 main directories, 1.distribution: The probability density generated from exploring the U and J parameter space using the Bayesian calibration assisted by a Markov chain Monte Carlo 2.evaluation_stage: Performance of the (U,J) from MCMC for the evaluation (FeO, α−Fe2O3, AlFeB2, Fe5PB2, Fe5SiB2) 3.exploration_stage: Performance of the (U,J) from MCMC for the exploration (Fe, Fe2P, Fe3Ge, BaFeO3, SrFeO3) 4.U3.8_J0.7: Performance of the (U,J) from Sasioglu et al PRB 2011.|