Published November 3, 2021 | Version v1
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Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

  • 1. Department of Physics and Astronomy, West Virginia University, Morgantown, WV, USA
  • 2. Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, USA
  • 3. Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA
  • 4. Facultad de Ingeniería, Benemérita Universidad Autónoma de Puebla, Apdo. Postal J-39, Puebla, Pue. 72570, México

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Description

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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References

Preprint
P. Tavadze, R. Boucher, G. Avendaño-Franco, K. X. Kocan, S. Singh, V. Dovale-Farelo, W. Ibarra-Hernández, M. B. Johnson, D. S. Mebane, A. H. Romero, arXiv:2109.07617 [Preprint.] (2021)