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Title: Finding new perovskite halides via machine learning

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 amore » result, the trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.« less
 [1] ;  [1] ;  [2] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Connecticut, Storrs, CT (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 2296-8016
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Frontiers in Materials
Additional Journal Information:
Journal Volume: 3; Journal ID: ISSN 2296-8016
Frontiers Research Foundation
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
Country of Publication:
United States
36 MATERIALS SCIENCE perovskites; informatics; support vector machines; formability; materials discovery