Finding New Perovskite Halides via Machine Learning

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

doi:10.3389/fmats.2016.00019

Abstract

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 towards 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) 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 181 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. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

Highlights

The materials community is currently witnessing a fundamental change in the way novel materials are designed and discovered
We find that the model with normalized features results in superior prediction performance and leads to a more physically meaningful finite formability region for perovskites
In principle, we were able to classify all of the 455 chemical compositions, going forward, we focus our attention on the top-40 ABX3 chemistries, all of which were classified as a perovskite with a probability ≥85%

Summary

Introduction

The materials community is currently witnessing a fundamental change in the way novel materials are designed and discovered. A steady increase in computational power, accompanied by developments in quantum theory and algorithmic breakthroughs that allow for efficient yet accurate quantum mechanical computations, opens the door to computing properties of a wide range of materials that once seemed prohibitively expensive. High-throughput explorations of the vast chemical space are increasingly being pursued and have significantly aided our intuition and knowledge-base of material properties (Ceder et al, 2011; Jain et al, 2011; Yu and Zunger, 2012; Curtarolo et al, 2013; Pilania et al, 2013, 2016; Sharma et al, 2014; Balachandran et al, 2016; Kim et al, 2016; Mannodi-Kanakkithodi et al, 2016). Big-data materials infrastructure (Service, 2012) is increasingly being built with the intent of knowledge extraction and rule-mining to identify candidate materials for next-generation materials breakthroughs.

Objectives

Results

Conclusion