2026-07-06T23:01:42Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1021 2021-11-03T14:44:06Z community-mcarchive openaire_data

Tavadze, Pedram Boucher, Reese Avendaño-Franco, Guillermo Kocan, Keenan X. Singh, Sobhit Dovale-Farelo, Viviana Ibarra-Hernández, Wilfredo Johnson, Matthew B Mebane, David S. Romero, Aldo H Tavadze, Pedram Boucher, Reese Avendaño-Franco, Guillermo Kocan, Keenan X. Singh, Sobhit Dovale-Farelo, Viviana Ibarra-Hernández, Wilfredo Johnson, Matthew B Mebane, David S. Romero, Aldo H 2021-11-03 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. text/markdown text/plain application/gzip https://doi.org/10.24435/materialscloud:16-d6 oai:materialscloud.org:1021 mcid:2021.188 eng Materials Cloud https://arxiv.org/abs/2109.07617 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:7q-yw info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode DFT+U Markov chain Monte Carlo MCMC Bayesian calibration Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling info:eu-repo/semantics/other