Literature DB >> 32393631

Self-interaction error overbinds water clusters but cancels in structural energy differences.

Kamal Sharkas1, Kamal Wagle2, Biswajit Santra2, Sharmin Akter3, Rajendra R Zope3, Tunna Baruah3, Koblar A Jackson4, John P Perdew2,5, Juan E Peralta4.   

Abstract

We gauge the importance of self-interaction errors in density functional approximations (DFAs) for the case of water clusters. To this end, we used the Fermi-Löwdin orbital self-interaction correction method (FLOSIC) to calculate the binding energy of clusters of up to eight water molecules. Three representative DFAs of the local, generalized gradient, and metageneralized gradient families [i.e., local density approximation (LDA), Perdew-Burke-Ernzerhof (PBE), and strongly constrained and appropriately normed (SCAN)] were used. We find that the overbinding of the water clusters in these approximations is not a density-driven error. We show that, while removing self-interaction error does not alter the energetic ordering of the different water isomers with respect to the uncorrected DFAs, the resulting binding energies are corrected toward accurate reference values from higher-level calculations. In particular, self-interaction-corrected SCAN not only retains the correct energetic ordering for water hexamers but also reduces the mean error in the hexamer binding energies to less than 14 meV/[Formula: see text] from about 42 meV/[Formula: see text] for SCAN. By decomposing the total binding energy into many-body components, we find that large errors in the two-body interaction in SCAN are significantly reduced by self-interaction corrections. Higher-order many-body errors are small in both SCAN and self-interaction-corrected SCAN. These results indicate that orbital-by-orbital removal of self-interaction combined with a proper DFA can lead to improved descriptions of water complexes.

Entities:  

Keywords:  DFT; SCAN meta-GGA; hydrogen bond; self-interaction; water

Year:  2020        PMID: 32393631      PMCID: PMC7260966          DOI: 10.1073/pnas.1921258117

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  56 in total

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Authors:  Sandeep K Reddy; Shelby C Straight; Pushp Bajaj; C Huy Pham; Marc Riera; Daniel R Moberg; Miguel A Morales; Chris Knight; Andreas W Götz; Francesco Paesani
Journal:  J Chem Phys       Date:  2016-11-21       Impact factor: 3.488

4.  Communication: self-interaction correction with unitary invariance in density functional theory.

Authors:  Mark R Pederson; Adrienn Ruzsinszky; John P Perdew
Journal:  J Chem Phys       Date:  2014-03-28       Impact factor: 3.488

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Journal:  Proc Natl Acad Sci U S A       Date:  2016-07-08       Impact factor: 11.205

6.  Conventional and Explicitly Correlated ab Initio Benchmark Study on Water Clusters: Revision of the BEGDB and WATER27 Data Sets.

Authors:  Debashree Manna; Manoj K Kesharwani; Nitai Sylvetsky; Jan M L Martin
Journal:  J Chem Theory Comput       Date:  2017-06-06       Impact factor: 6.006

7.  Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects.

Authors:  Ondrej Marsalek; Thomas E Markland
Journal:  J Phys Chem Lett       Date:  2017-03-22       Impact factor: 6.475

8.  Density and Compressibility of Liquid Water and Ice from First-Principles Simulations with Hybrid Functionals.

Authors:  Alex P Gaiduk; François Gygi; Giulia Galli
Journal:  J Phys Chem Lett       Date:  2015-07-13       Impact factor: 6.475

9.  First-Principles Calculation of Water pKa Using the Newly Developed SCAN Functional.

Authors:  Ruiyu Wang; Vincenzo Carnevale; Michael L Klein; Eric Borguet
Journal:  J Phys Chem Lett       Date:  2019-12-13       Impact factor: 6.475

Review 10.  Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions.

Authors:  Gerardo Andrés Cisneros; Kjartan Thor Wikfeldt; Lars Ojamäe; Jibao Lu; Yao Xu; Hedieh Torabifard; Albert P Bartók; Gábor Csányi; Valeria Molinero; Francesco Paesani
Journal:  Chem Rev       Date:  2016-05-17       Impact factor: 60.622

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2.  Machine learning potentials for complex aqueous systems made simple.

Authors:  Christoph Schran; Fabian L Thiemann; Patrick Rowe; Erich A Müller; Ondrej Marsalek; Angelos Michaelides
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