Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

Pikee Priya,N R Aluru

doi:10.1038/s41524-021-00551-3

Pikee Priya, N R Aluru

Open Access

https://doi.org/10.1038/s41524-021-00551-3

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Abstract

We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites and their classification based on the type of charge carrier at different conditions of temperature and environment. After evaluating a set of >100 features, we identify average ionic radius, minimum electronegativity, minimum atomic mass, minimum formation energy of oxides for all B-site, and B-site dopant ions of the perovskite as the crucial and relevant predictors for determining conductivity and the type of charge carriers. The models are validated by predicting the conductivity of compounds absent in the training set. We screen 1793 undoped and 95,832 A-site and B-site doped perovskites to report the perovskites with high conductivities, which can be used for different energy applications, depending on the type of the charge carriers.

Highlights

Advances in technology and science rely on the screening and discovery of high-performance and novel functional materials
Most of these applications currently are limited to high temperatures (800–1000 °C), where the kinetics of various chemical reactions and transport of charge carriers are relatively fast
We aim to accelerate this process through machine learning (ML). b The flow chart for the methodology adopted in this work

Summary

Introduction

Advances in technology and science rely on the screening and discovery of high-performance and novel functional materials. Green energy technologies like the fuel and electrolyzer cells, alkali-ion batteries, gas (e.g., carbon dioxide and oxygen) sensors, gas separation membranes, membrane reactors, and solar cells, all rely on total conductivity be it of the type ionic, electronic, or mixed[3,4,5,6]. Dense proton-conducting membranes are used in membrane reactors for dehydrogenation of gaseous hydrocarbons to produce clean hydrogen fuel[12,13] Most of these applications currently are limited to high temperatures (800–1000 °C), where the kinetics of various chemical reactions and transport of charge carriers are relatively fast. The general trend for progress has been diagonally upward toward regions of higher conductivity and lower temperature This has been made possible through extensive experiments by researchers. Statistical data-driven methods can accelerate this process through high-throughput screening of materials

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