Explainable Machine Learning for Oxygen Diffusion in Perovskites and Pyrochlores

TL;DR

This study uses explainable machine learning to identify key features influencing oxygen diffusion activation energy in perovskites and pyrochlores, enabling rapid material screening.

Contribution

It introduces an explainable ML approach that highlights important material features for oxygen diffusion, revealing surprising insights about property correlations.

Findings

Ionicity of A-site bond is crucial for perovskites.

A-site s valence electron count is key for pyrochlores.

Weighted averages of elemental properties are most predictive.

Abstract

Explainable machine learning can help to discover new physical relationships for material properties. To understand the material properties that govern the activation energy for oxygen diffusion in perovskites and pyrochlores, we build a database of experimental activation energies and apply a grouping algorithm to the material property features. These features are then used to fit seven different machine learning models. An ensemble consensus determines that the most important features for predicting the activation energy are the ionicity of the A-site bond and the partial pressure of oxygen for perovskites. For pyrochlores, the two most important features are the A-site $s$ valence electron count and the B-site electronegativity. The most important features are all constructed using the weighted averages of elemental metal properties, despite weighted averages of the constituent binary oxides being included in our feature set. This is surprising because the material properties of the constituent oxides are more similar to the experimentally measured properties of perovskites and pyrochlores than the features of the metals that are chosen. The easy-to-measure features identified in this work enable rapid screening for new materials with fast oxide-ion diffusivity.