Data-driven atomic environment prediction for binaries using the Mendeleev number

Villars, P.; Cenzual, K.; Daams, J.; Chen, Y.; Iwata, Shuichi

doi:10.1016/j.jallcom.2003.08.060

Cited by 64 publications

(41 citation statements)

References 5 publications

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“…It visualizes clearly that, with the exception of those of H, Be and Mg, the values are just slightly different (see also Tables 2a and 2b). showed so far to be the most effective ones among the derived elemental-property parameters for separating compound formers from non-formers in multinary systems [17], as well as in structure maps [18]. SZ aME (SZ aMD ) and RE aME (RE aMD ) fit very well in the general trend of the pattern behaviors shown in Figs.…”

Section: Figmentioning

confidence: 58%

“…Some variants were found to be of similar efficiency. Still another variant was recently successfully applied in order to separate different atomic-environment types (AET) of the constituents in equiatomic binary compounds [18]. In most of the above cases the periodic-number-like scale or PN was then called 'Mendeleev number'.…”

Section: Fundamental Elemental-property Parameters: Atomic Number An mentioning

confidence: 99%

See 1 more Smart Citation

A new approach to describe elemental-property parameters

Villars¹,

Daams²,

Shikata³

et al. 2008

Chem. Met. Alloys

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The atomic number (AN) of the elements together with their 'periodic number' (PN) were found to form an efficient pair for the discussion of metallurgical and structural problems. The periodic number PN represents a different enumeration of the elements, emphasizing the role of the valence electrons. In contrast to the atomic number, PN depends in details on the underlying Periodic Table of the elements. As a first result we describe the elemental-property parameters 'atomic size SZ a ' and 'atomic reactivity RE a ', derived from fits to various experimental and theoretical data sets. These two parameters can be approximated as simple functions of AN and PN: SZ a = k SZ [log (AN + 1)] [k PN-(log PN) 3 ], RE a = k RE {[log (AN + 1)] [k PN-(log PN) 3 ]}-1 = k SZ k RE (SZ a)-1 , where k PN is a scaling factor, and k SZ , k RE are fit parameters for a fit to experimental data. We argue that all elemental-property parameter patterns are derived from AN and PN. AN and PN represent fundamental elemental-property parameters independent from each other. Any pattern, which shows well-defined functional behavior within each group number GN, as well as within each main quantum number QN, can be included. On the example of compound formers/non-formers in binary, ternary and quaternary chemical systems we demonstrate that a quantitative link exists between material properties and AN, PN (or simple functions of both) of the constituent elements.

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Section: Figmentioning

confidence: 58%

Section: Fundamental Elemental-property Parameters: Atomic Number An mentioning

confidence: 99%

A new approach to describe elemental-property parameters

Villars¹,

Daams²,

Shikata³

et al. 2008

Chem. Met. Alloys

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show abstract

“…Nearly twenty years after the advocacy of this structure mapping approach, Villars et al 101,102 used Mendeleev number for the prediction of compound former/nonformer in any binary, ternary and quaternary systems. They reported that the Mendeleev number is a highly effective feature compared to atomic number for this structure prediction method.…”

Section: Structure Mappingmentioning

confidence: 99%

Discovering and Designing Bulk Metallic Glasses

Ranganathan¹,

Biswas²,

Subramaniam³

2009

Properties and Applications of Complex Intermetallics

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The discovery of metallic glasses and bulk metallic glasses (BMG) were important landmarks in materials research in recent times. Starting with an overview of the techniques for the synthesis of BMG, this paper will critically review the literature on glass forming criteria, glass forming compositions and structural and thermodynamic models describing glasses. With an explosion in the number of compositions forming BMG, their classification becomes important, which will help understand the relationship amongst the various BMG. Following the standard classification scheme based on compositions, Pettifor's approach using Mendeleev number and the importance of bond orbitals in the classification of BMG is highlighted.

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“…Structures are described by their structure types in our studies, whereas more general characterizations via atomic environment types and the ensuing statistical analysis have been performed by Villars et al (2004) and Villars, Daams, Shikata, Chen & Iwata (2008).…”

Section: Introductionmentioning

confidence: 99%

“…2 An alternative elemental parameter -the 'periodic number' PN -was introduced a few years ago (Villars, Daams, Shikata, Rajan & Iwata, 2008); it scans the periodic table of elements not in horizontal lines, as the atomic number does, but in vertical lines, and thus conforms to the common properties that atoms within one group of the periodic table usually share. Based on this parameter, a recent publication also offers statistical considerations (Villars & Iwata, 2013).…”

mentioning

confidence: 99%

More statistics on intermetallic compounds – ternary phases

Dshemuchadse

Steurer

2015

Acta Cryst Sect A

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How many different intermetallic compounds are known so far, and in how many different structure types do they crystallize? What are their chemical compositions, the most abundant ones and the rarest ones? These are some of the questions we are trying to find answers for in our statistical analysis of the structures of the 20 829 intermetallic phases included in the databasePearson's Crystal Data, with the goal of gaining insight into some of their ordering principles. In the present paper, we focus on the subset of 13 026 ternary intermetallics, which crystallize in 1391 different structure types; remarkably, 667 of them have just one representative. What makes these 667 structures so unique that they are not adopted by any other of the known intermetallic compounds? Notably, ternary compounds are known in only 5109 of the 85 320 theoretically possible ternary intermetallic systems so far. In order to get an overview of their chemical compositions we use structure maps with Mendeleev numbers as ordering parameters.

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Data-driven atomic environment prediction for binaries using the Mendeleev number

Cited by 64 publications

References 5 publications

A new approach to describe elemental-property parameters

A new approach to describe elemental-property parameters

Discovering and Designing Bulk Metallic Glasses

More statistics on intermetallic compounds – ternary phases

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