Bond-valence parameters for solids

Brese, N. E.; O’Keeffe, Michael

doi:10.1107/s0108768190011041

Cited by 7,156 publications

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“…For this, preliminary valence contributions were evaluated using literature R 0 parameters (Brese & O’Keeffe, 1991). Using this criterion, (i) can be transformed into a distance cutoff d cutoff = R 0 – b [ln(0.05) + ln( S i )].…”

Section: Methodsmentioning

confidence: 99%

“…Two data sets, corresponding to iron(II) and iron(III), were used as input to separately optimize the corresponding R 0 parameters, with initial values adopted from the literature (Brese & O’Keeffe, 1991). For a single heteroleptic site, the set of R 0 values that satisfies the bond-valence sum rule is not unique.…”

Section: Methodsmentioning

confidence: 99%

“…During the investigation of metal ion-binding architectures in proteins (Zheng et al , 2008, 2014), we successfully employed the bond-valence model to check the quality of metal-binding site modeling in low-resolution structures. Initially, we used reference literature values for bond-valence ( R 0 ) parameters, which were derived two decades ago from manually curated structures and extrapolated linear relationships between bond-valence contributions (Brese & O’Keeffe, 1991; Brown & Altermatt, 1985). However, in cases involving iron and nitrogen, such as structures containing heme, we consistently obtained bond-valence sums that were significantly different from known oxidation states, prompting us to attempt a re-evaluation of bond-valence R 0 parameters for iron-binding sites.…”

Section: Introductionmentioning

confidence: 99%

“…Following the initial publication of bond-valence parameters based on inorganic crystal structures (Brese & O’Keeffe, 1991; Brown & Altermatt, 1985), numerous studies have discussed the bond-valence model in the context of small-molecule crystal structures in the Cambridge Structural Database (CSD; Allen, 2002; Groom et al , 2016) as well as in the Inorganic Crystal Structure Database (ICSD; Bergerhoff & Brandenburg, 2004). Some notable results based on CSD data include bond-valence parameters for copper (Shields et al , 2000), lanthanides (Trzesowska et al , 2004, 2006), cadmium (Palenik, 2006), antimony (Palenik et al , 2005) and ammonium (García-Rodríguez et al , 2000).…”

Section: Introductionmentioning

confidence: 99%

“…It has been noted before (Brown, 2009) that the bond-valence parameter R 0 derived from different set of references structures differs by as much as 0.05 Å. With this in mind, we cautiously evaluated differences compared with reference values (Brown & Altermatt, 1985; Brese & O’Keeffe, 1991), and considered them to be significant only when they were larger than ±0.1 Å. As a control, the same protocol was applied to several other metals (Na, Mg, K, Ca and Zn), and in all these cases our values derived from the same data-driven procedure agreed with the previous parameters within statistical error.…”

Section: Introductionmentioning

confidence: 99%

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Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography

Zheng

Langner

Shields

et al. 2017

Acta Crystallogr D Struct Biol

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The bond-valence model is a reliable way to validate assumed oxidation states based on structural data. It has successfully been employed for analyzing metal-binding sites in macromolecule structures. However, inconsistent results for heme-based structures suggest that some widely used bond-valence R0 parameters may need to be adjusted in certain cases. Given the large number of experimental crystal structures gathered since these initial parameters were determined and the similarity of binding sites in organic compounds and macromolecules, the Cambridge Structural Database (CSD) is a valuable resource for refining metal–organic bond-valence parameters. R0 bond-valence parameters for iron(II), iron(III) and other metals have been optimized based on an automated processing of all CSD crystal structures. Almost all R0 bond-valence parameters were reproduced, except for iron–nitrogen bonds, for which distinct R0 parameters were defined for two observed subpopulations, corresponding to low-spin and high-spin states, of iron in both oxidation states. The significance of this data-driven method for parameter discovery, and how the spin state affects the interpretation of heme-containing proteins and iron-binding sites in macromolecular structures, are discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data mining of iron(II) and iron(III) bond-valence parameters, and their relevance for macromolecular crystallography

Zheng

Langner

Shields

et al. 2017

Acta Crystallogr D Struct Biol

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Calculation of chemical bond parameters in La1?xCaxCrO3 (0.0?x?0.3)

Zhang

1999

Int. J. Quant. Chem.

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ABSTRACT:The chemical bond parameters, that is, bond covalency, bond Ž susceptibility, and macroscopic linear susceptibility of La Ca CrO x s 0.0, 0.1, 0.2, 1yx x 3 . 0.3 has been calculated using a semiempirical method. This method is the generalization of the dielectric description theory proposed by Phillips, Van Vechten, Levine, and Ž . Tanaka PVLT . In the calculation of bond valence, two schemes were adopted. One is the Ž . bond valence sums BVS scheme, and the other is the equal-valence scheme. Both schemes suggest that for the title compounds bond covalency and bond susceptibility are mainly influenced by bond valence and are insensitive to the Ca doping level or structural change. Larger bond valences usually result in higher bond covalency and Ž bond susceptibility. The macroscopic linear susceptibility increases only slightly for BVS . scheme with the increasing Ca doping level.

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