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Pure non-local machine-learned density functional theory for electron correlation

Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in...

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Autores principales: Margraf, Johannes T., Reuter, Karsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804195/
https://www.ncbi.nlm.nih.gov/pubmed/33436595
http://dx.doi.org/10.1038/s41467-020-20471-y
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author Margraf, Johannes T.
Reuter, Karsten
author_facet Margraf, Johannes T.
Reuter, Karsten
author_sort Margraf, Johannes T.
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description Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron self-interaction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the mean-field computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation.
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spelling pubmed-78041952021-01-21 Pure non-local machine-learned density functional theory for electron correlation Margraf, Johannes T. Reuter, Karsten Nat Commun Article Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron self-interaction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the mean-field computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation. Nature Publishing Group UK 2021-01-12 /pmc/articles/PMC7804195/ /pubmed/33436595 http://dx.doi.org/10.1038/s41467-020-20471-y Text en © The Author(s) 2021 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Margraf, Johannes T.
Reuter, Karsten
Pure non-local machine-learned density functional theory for electron correlation
title Pure non-local machine-learned density functional theory for electron correlation
title_full Pure non-local machine-learned density functional theory for electron correlation
title_fullStr Pure non-local machine-learned density functional theory for electron correlation
title_full_unstemmed Pure non-local machine-learned density functional theory for electron correlation
title_short Pure non-local machine-learned density functional theory for electron correlation
title_sort pure non-local machine-learned density functional theory for electron correlation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804195/
https://www.ncbi.nlm.nih.gov/pubmed/33436595
http://dx.doi.org/10.1038/s41467-020-20471-y
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