Proposed definition of crystal substructure and substructural similarity

Yang, Lusann; Dacek, Stephen; Ceder, Gerbrand

doi:10.1103/physrevb.90.054102

Cited by 36 publications

(37 citation statements)

References 24 publications

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“…For instance, beyond the description of materials by space groups, a description by crystal structure prototypes can be essential and several algorithms have been developed 63,64 and applied toward probabilistic crystal structure prediction models. 65,66 Further methods include the development of the "symmetry functions" that capture two and three-body terms 67 and local environment descriptors 68 to characterize bonding details in crystals. "Fingerprints" representing the somewhat complex object of crystal structures have also been proposed to characterize the landscape of possible structures and as descriptors in data mining approaches.…”

Section: A Descriptors For Materials Structure and Propertiesmentioning

confidence: 99%

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

et al. 2016

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Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery, finance, medicine, and marketing, and has the potential to similarly advance materials science. In this paper, we describe advances in simulation-based materials databases, open-source software tools, and machine learning algorithms that are converging to create new opportunities for materials informatics. We discuss the data mining techniques of exploratory data analysis, clustering, linear models, kernel ridge regression, tree-based regression, and recommendation engines. We present these techniques in the context of several materials application areas, including compound prediction, Li-ion battery design, piezoelectric materials, photocatalysts, and thermoelectric materials. Finally, we demonstrate how new data and tools are making it easier and more accessible than ever to perform data mining through a new analysis that learns trends in the valence and conduction band character of compounds in the Materials Project database using data on over 2500 compounds.Materials science has traditionally been driven by scientific intuition followed by experimental study. In recent years, theory and computation have provided a secondary avenue for materials property prediction and design. Several successful examples of materials designed in a computer and then realized in the laboratory 1 have now established such methods as a new route for materials discovery and optimization. As computational methods approach maturity, new and complementary techniques based on statistical analysis and machine learning are poised to revolutionize materials science.While the modern use of the term materials informatics dates back only a decade ago, 2 the use of an informatics approach to chemistry and materials science is as old as the periodic table. When Mendeleev grouped together elements by their properties, the electron was yet to be discovered, and the principles of electron configuration and quantum mechanics that underpin chemistry were still many decades away. However, Mendeleev's approach not only resulted in a useful classification but could also make predictions: missing positions in the periodic table indicated potential new elements that were later confirmed experimentally. Mendeleev was also able to spot inaccuracies in atomic weight data of the time. Today, the search for patterns in data remains the goal of materials informatics, although the tools have evolved considerably since Mendeleev's work.While materials informatics methods are still in their infancy compared to other fields, advancements in materials databases and software in the last decade are rapidly gaining ground. In this paper, we discuss recent developments of materials informatics, concentrating specifically on connecting a material's crystal structure and its composition to its properties (and ignoring, for instance, microstructure and processing). First, we provide a brief history of classic studies based on mining crystallographic databases. Next, we describe the recent...

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Section: A Descriptors For Materials Structure and Propertiesmentioning

confidence: 99%

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

et al. 2016

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“…Atomic and molecular environment descriptors are also needed in the context of machine learning schemes for force fields, [36][37][38] bonding pattern recognition, 39 or to compare vacancy, interstitial, and intercalation sites. 40 These descriptors could also be used to measure similarities between structures. Even though they have never been used in this context, we will present a comparison with such a descriptor.…”

Section: Introductionmentioning

confidence: 99%

A fingerprint based metric for measuring similarities of crystalline structures

Zhu

Amsler

Fuhrer

et al. 2016

The Journal of Chemical Physics

130

124

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Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings. C 2016 AIP Publishing LLC. [http://dx

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“…The structural and substructural similarity depends on the similarity of the coordination environment between two substructures. 95,97 The coordination environment tends to be similar among crystals of similar chemistries. 99 By quantifying the similarity of the predicted SnN structures to the observed Sn 3 N 4 structure, we identify the candidate SnN structure that possesses the most similar substructure coordination to Sn 3 N 4 .…”

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confidence: 99%

Synthesis of a mixed-valent tin nitride and considerations of its possible crystal structures

Caskey

Holder

Shulda

et al. 2016

The Journal of Chemical Physics

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Recent advances in theoretical structure prediction methods and high-throughput computational techniques are revolutionizing experimental discovery of the thermodynamically stable inorganic materials. Metastable materials represent a new frontier for studies, since even simple binary non ground state compounds of common elements may be awaiting discovery. However, there are significant research challenges related to non-equilibrium thin film synthesis and crystal structure predictions, such as small strained crystals in the experimental samples and energy minimization based theoretical algorithms. Here we report on experimental synthesis and characterization, as well as theoretical first-principles calculations of a previously unreported mixed-valent binary tin nitride. Thin film experiments indicate that this novel material is Ndeficient SnN with tin in the mixed II/IV valence state and a small low-symmetry unit cell. Theoretical calculations suggest that the most likely crystal structure has the space group 2 (SG2) related to the distorted delafossite (SG166), which is nearly 0.1 eV/atom above the ground state SnN polymorph. This observation is rationalized by the structural similarity of the SnN distorted delafossite to the chemically related Sn 3 N 4 spinel compound, which provides a fresh scientific insight into the reasons for growth of polymorphs of the metastable material. In addition to reporting on the discovery of the simple binary SnN compound, this paper illustrates a possible way of combining a wide range of advanced characterization techniques with the first-principle property calculation methods, to elucidate the most likely crystal structure of the previously unreported metastable materials. 2

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Proposed definition of crystal substructure and substructural similarity

Cited by 36 publications

References 24 publications

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

A fingerprint based metric for measuring similarities of crystalline structures

Synthesis of a mixed-valent tin nitride and considerations of its possible crystal structures

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