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

@article{Tavadze2021ExploringDP,
  title={Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling},
  author={Pedram Tavadze and Reese Boucher and Guillermo Avenda{\~n}o-Franco and Keenan X. Kocan and Sobhit Singh and Viviana Dovale-Farelo and Wilfredo Ibarra-Hern'andez and Matthew B. Johnson and David S. Mebane and Aldo H. Romero},
  journal={npj Computational Materials},
  year={2021},
  volume={7},
  pages={1-9}
}
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… Expand

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