4.8 Article

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

Journal

NPJ COMPUTATIONAL MATERIALS
Volume 7, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41524-021-00651-0

Keywords

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Funding

  1. National Science Foundation (NSF) [ACI-1053575]
  2. Texas Advanced Computing Center
  3. Pittsburgh Supercomputing Center
  4. NSF Major Research Instrumentation Program (MRI) Award [MRI-1726534]
  5. O'Brien Fund of the WVU Energy Institute
  6. Summer Undergraduate Research Experience (SURE) at WVU
  7. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-SC0021375]
  8. U.S. Department of Energy (DOE) [DE-SC0021375] Funding Source: U.S. Department of Energy (DOE)

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The authors conducted a study on a group of iron-based compounds to explore the effects of U and J parameters on predicting physical properties, finding that the PBE functional has the smallest standard deviation and the most transferable parameters.
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.

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