Finding new perovskite halides via machine learning

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

doi:10.3389/fmats.2016.00019

Title: Finding new perovskite halides via machine learning

Journal Article · Tue Apr 26 00:00:00 EDT 2016 · Frontiers in Materials

DOI:https://doi.org/10.3389/fmats.2016.00019· OSTI ID:1258584

Pilania, Ghanshyam ^[1]; Balachandran, Prasanna V. ^[1]; Kim, Chiho ^[2]; Lookman, Turab ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Connecticut, Storrs, CT (United States)

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 ABX₃ 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 ABX₃ 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. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX₃ compositions with perovskite crystal structure.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1258584

Report Number(s):: LA-UR-16-21643

Journal Information:: Frontiers in Materials, Vol. 3; ISSN 2296-8016

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 118 works

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