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

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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