Literature DB >> 26938575

How Much Can Density Functional Approximations (DFA) Fail? The Extreme Case of the FeO4 Species.

Wei Huang1, Deng-Hui Xing1, Jun-Bo Lu1, Bo Long1, W H Eugen Schwarz1, Jun Li1.   

Abstract

A thorough theoretical study of the relative energies of various molecular Fe·4O isomers with different oxidation states of both Fe and O atoms is presented, comparing simple Hartree-Fock through many Kohn-Sham approximations up to extended coupled cluster and DMRG multiconfiguration benchmark methods. The ground state of Fe·4O is a singlet, hexavalent iron(VI) complex (1)C2v-[Fe(VI)O2](2+)(O2)(2-), with isomers of oxidation states Fe(II), Fe(III), Fe(IV), Fe(V), and Fe(VIII) all lying slightly higher within the range of 1 eV. The disputed existence of oxidation state Fe(VIII) is discussed for isolated FeO4 molecules. Density functional theory (DFT) at various DF approximation (DFA) levels of local and gradient approaches, Hartree-Fock exchange and meta hybrids, range dependent, DFT-D and DFT+U models do not perform better for the relative stabilities of the geometric and electronic Fe·4O isomers than within 1-5 eV. The Fe·4O isomeric species are an excellent testing and validation ground for the development of density functional and wave function methods for strongly correlated multireference states, which do not seem to always follow chemical intuition.

Entities:  

Year:  2016        PMID: 26938575     DOI: 10.1021/acs.jctc.5b01040

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  3 in total

1.  The high covalence of metal-ligand bonds as stability limiting factor: the case of Rh(IX)O4+ and Rh(IX)NO3.

Authors:  Mateusz A Domański; Łukasz Wolański; Paweł Szarek; Wojciech Grochala
Journal:  J Mol Model       Date:  2020-02-07       Impact factor: 1.810

2.  Machine learning magnetism classifiers from atomic coordinates.

Authors:  Helena A Merker; Harry Heiberger; Linh Nguyen; Tongtong Liu; Zhantao Chen; Nina Andrejevic; Nathan C Drucker; Ryotaro Okabe; Song Eun Kim; Yao Wang; Tess Smidt; Mingda Li
Journal:  iScience       Date:  2022-09-28

3.  Predicting electronic structure properties of transition metal complexes with neural networks.

Authors:  Jon Paul Janet; Heather J Kulik
Journal:  Chem Sci       Date:  2017-05-17       Impact factor: 9.825

  3 in total

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