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Prediction and Calculation of Crystal Structures pp 139–179Cite as

Data Mining Approaches to High-Throughput Crystal Structure and Compound Prediction

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Part of the book series: Topics in Current Chemistry ((TOPCURRCHEM,volume 345))

Abstract

Predicting unknown inorganic compounds and their crystal structure is a critical step of high-throughput computational materials design and discovery. One way to achieve efficient compound prediction is to use data mining or machine learning methods. In this chapter we present a few algorithms for data mining compound prediction and their applications to different materials discovery problems. In particular, the patterns or correlations governing phase stability for experimental or computational inorganic compound databases are statistically learned and used to build probabilistic or regression models to identify novel compounds and their crystal structures. The stability of those compound candidates is then assessed using ab initio techniques. Finally, we report a few cases where data mining driven computational predictions were experimentally confirmed through inorganic synthesis.

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Authors and Affiliations

  1. Institute of Condensed Matter and Nanosciences (IMCN), Université Catholique de Louvain, Chemin des étoiles 8, bte L7.03.01, 1348, Louvain-la-Neuve, Belgium

    Geoffroy Hautier

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  1. Geoffroy Hautier
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Editors and Affiliations

  1. Dept. of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Sule Atahan-Evrenk

  2. Dept. of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA

    Alan Aspuru-Guzik

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Hautier, G. (2013). Data Mining Approaches to High-Throughput Crystal Structure and Compound Prediction. In: Atahan-Evrenk, S., Aspuru-Guzik, A. (eds) Prediction and Calculation of Crystal Structures. Topics in Current Chemistry, vol 345. Springer, Cham. https://doi.org/10.1007/128_2013_486

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