Physics Commons^™

Exploring Dft+U Parameter Space With A Bayesian Calibration Assisted By Markov Chain Monte Carlo Sampling, Pedram Tavadze, Reese Boucher, Guillermo Avendaño-Franco, Keenan X. Kocan, Sobhit Singh, Viviana Dovale-Farelo, Wilfredo Ibarra-Hernández, Matthew B. Johnson, David S. Mebane, Aldo H. Romero

Faculty & Staff Scholarship

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 …