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The 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 correction to treat strongly correlated electronic states. Unfortunately, the 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.

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling