Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
Author ORCID Identifier
Eberly College of Arts and Sciences
Physics and Astronomy
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.
Digital Commons Citation
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., "Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling" (2021). Faculty & Staff Scholarship. 3060.
Tavadze, P., Boucher, R., Avendaño-Franco, G. et al. Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling. npj Comput Mater 7, 182 (2021). https://doi.org/10.1038/s41524-021-00651-0
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This article received support from the WVU Libraries' Open Access Author Fund.