On the crystal chemistry of normal valence compounds

Mooser, E.; Pearson, W. B.

doi:10.1107/s0365110x59002857

Cited by 273 publications

(115 citation statements)

References 2 publications

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“…Many researchers have constructed empirical "rules" that "explain" the adoption of particular structures as a function of a few variables. These include the early works of Hume-Rothery [8], Mooser and Pearson [9], Miedema [10], and Brewer [11]. More recently, using an extensive database, Villars and collaborators also found that the adoption of a particular structure type could be "predicted" (with about 95% confidence, but of course, retrospectively).…”

Section: "Simple" Challenges: Composition and Structurementioning

confidence: 99%

Challenges and opportunities in solid-state chemistry

DiSalvo

2000

Pure and Applied Chemistry

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Abstract:We are largely unable to predict the composition and crystal structure of new extended structure compounds. Yet we find that as materials become more complex in stoichiometry and structure, new, interesting, and perhaps useful, phenomena are encountered. The need for improved experimental and theoretical approaches to seize more rapidly the opportunities provided in this complexity is discussed.

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Section: "Simple" Challenges: Composition and Structurementioning

confidence: 99%

Challenges and opportunities in solid-state chemistry

DiSalvo

2000

Pure and Applied Chemistry

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“…First, Mooser and Perason [22] applied a two-dimension graphic to study the stability of different compounds, the two factors they used were the difference of electronegativity between the cation and the anion and the average principle quantum number. They succeeded to discriminate the crystal structures of AB-type compounds, of AX 2 -type halides, and the metallic or non-metallic ternary fluorides ABX.…”

Section: Structural Map Methodsmentioning

confidence: 99%

Prediction of Formability in Perovskite-Type Oxides~!2008-08-05~!2008-10-08~!2008-12-05~!

Kumar¹,

Verma²,

Bhardwaj³

2008

TOAPJ

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Prediction criterions for the formabilities of perovskite-type oxides are obtained by using the empirical structure map methods constructed by two parameters octahedral factor (r B /r O ) and tolerance factor, on this structure map, simple point representing systems of forming and non forming are distributed in distinctively different regions. In this paper we found that the octahedral factor, is as important as the tolerance factor with regards to the formability of perovskitetype oxides. We have applied the proposed model to 173 ABO 3 perovskites and it can be used to search for new perovskite-type oxides by screening all possible elemental combinations.

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“…These studies performed a classification into perovskite or non-perovskite in the traditional way by using a structure map. A structure map is defined as a two-dimensional plot of the values of two features of the known solids and with lines drawn using ad hoc principles (including by hand) that separate the data points into the different classes of crystal structures (Mooser and Pearson, 1959). The tolerance and octahedral factors are the two most widely used structure governing features in these plots (Li et al, 2004Zhang et al, 2007;Feng et al, 2008;Kumar et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Finding New Perovskite Halides via Machine Learning

et al. 2016

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Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

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On the crystal chemistry of normal valence compounds

Cited by 273 publications

References 2 publications

Challenges and opportunities in solid-state chemistry

Challenges and opportunities in solid-state chemistry

Prediction of Formability in Perovskite-Type Oxides~!2008-08-05~!2008-10-08~!2008-12-05~!

Finding New Perovskite Halides via Machine Learning

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