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

Abstract 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 Hub...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/406216400ae242adb9a0f8c600fbfa6a
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spelling	oai:doaj.org-article:406216400ae242adb9a0f8c600fbfa6a2021-11-14T12:15:30ZExploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling10.1038/s41524-021-00651-02057-3960https://doaj.org/article/406216400ae242adb9a0f8c600fbfa6a2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00651-0https://doaj.org/toc/2057-3960Abstract 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.Pedram TavadzeReese BoucherGuillermo Avendaño-FrancoKeenan X. KocanSobhit SinghViviana Dovale-FareloWilfredo Ibarra-HernándezMatthew B. JohnsonDavid S. MebaneAldo H. RomeroNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 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 Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
description	Abstract 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.
format	article
author	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
author_facet	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
author_sort	Pedram Tavadze
title	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
title_short	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
title_full	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
title_fullStr	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
title_full_unstemmed	Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
title_sort	exploring dft+u parameter space with a bayesian calibration assisted by markov chain monte carlo sampling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/406216400ae242adb9a0f8c600fbfa6a
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Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

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