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- W3200417421 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." @default.
- W3200417421 created "2021-09-27" @default.
- W3200417421 creator A5003756148 @default.
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- W3200417421 creator A5070005859 @default.
- W3200417421 creator A5089787597 @default.
- W3200417421 date "2021-09-15" @default.
- W3200417421 modified "2023-09-25" @default.
- W3200417421 title "Exploring DFT$+U$ parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling" @default.
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