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A framework for quantifying uncertainty in DFT energy corrections

In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-deri...

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Autores principales: Wang, Amanda, Kingsbury, Ryan, McDermott, Matthew, Horton, Matthew, Jain, Anubhav, Ong, Shyue Ping, Dwaraknath, Shyam, Persson, Kristin A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322326/
https://www.ncbi.nlm.nih.gov/pubmed/34326361
http://dx.doi.org/10.1038/s41598-021-94550-5
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author Wang, Amanda
Kingsbury, Ryan
McDermott, Matthew
Horton, Matthew
Jain, Anubhav
Ong, Shyue Ping
Dwaraknath, Shyam
Persson, Kristin A.
author_facet Wang, Amanda
Kingsbury, Ryan
McDermott, Matthew
Horton, Matthew
Jain, Anubhav
Ong, Shyue Ping
Dwaraknath, Shyam
Persson, Kristin A.
author_sort Wang, Amanda
collection PubMed
description In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.
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spelling pubmed-83223262021-07-30 A framework for quantifying uncertainty in DFT energy corrections Wang, Amanda Kingsbury, Ryan McDermott, Matthew Horton, Matthew Jain, Anubhav Ong, Shyue Ping Dwaraknath, Shyam Persson, Kristin A. Sci Rep Article In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability. Nature Publishing Group UK 2021-07-29 /pmc/articles/PMC8322326/ /pubmed/34326361 http://dx.doi.org/10.1038/s41598-021-94550-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Amanda
Kingsbury, Ryan
McDermott, Matthew
Horton, Matthew
Jain, Anubhav
Ong, Shyue Ping
Dwaraknath, Shyam
Persson, Kristin A.
A framework for quantifying uncertainty in DFT energy corrections
title A framework for quantifying uncertainty in DFT energy corrections
title_full A framework for quantifying uncertainty in DFT energy corrections
title_fullStr A framework for quantifying uncertainty in DFT energy corrections
title_full_unstemmed A framework for quantifying uncertainty in DFT energy corrections
title_short A framework for quantifying uncertainty in DFT energy corrections
title_sort framework for quantifying uncertainty in dft energy corrections
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322326/
https://www.ncbi.nlm.nih.gov/pubmed/34326361
http://dx.doi.org/10.1038/s41598-021-94550-5
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