Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

TL;DR

This paper introduces an unsupervised deep learning approach to identify crystal structure information from disordered perovskite compositions, enabling efficient discovery of new materials and understanding phase behaviors.

Contribution

It presents a novel unsupervised fingerprinting method that captures crystal structure features from composition data, surpassing supervised models in predicting perovskite formability and facilitating analogical discovery.

Findings

Unsupervised fingerprints encode crystal symmetry information.

The method predicts crystal structures with higher accuracy than supervised models.

Screened ~600,000 compounds with a 94% success rate for promising perovskites.

Abstract

Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in (A1-xA'x)BO3 and A(B1-xB'x)O3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.