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

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Tavadze, Pedram</dc:creator>
  <dc:creator>Boucher, Reese</dc:creator>
  <dc:creator>Avendaño-Franco, Guillermo</dc:creator>
  <dc:creator>Kocan, Keenan X.</dc:creator>
  <dc:creator>Singh, Sobhit</dc:creator>
  <dc:creator>Dovale-Farelo, Viviana</dc:creator>
  <dc:creator>Ibarra-Hernández, Wilfredo</dc:creator>
  <dc:creator>Johnson, Matthew B</dc:creator>
  <dc:creator>Mebane, David S.</dc:creator>
  <dc:creator>Romero, Aldo H</dc:creator>
  <dc:date>2021-11-03</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2021.188</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:16-d6</dc:identifier>
  <dc:identifier>mcid:2021.188</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1021</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>DFT+U</dc:subject>
  <dc:subject>Markov chain Monte Carlo</dc:subject>
  <dc:subject>MCMC</dc:subject>
  <dc:subject>Bayesian calibration</dc:subject>
  <dc:title>Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>