摘要
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 (A_(1-x)A'_(x))BO_(3) and A(B_(1-x)B'_(x))O_(3) 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.
基金
The authors thank Sachini Amararathna for collecting the experimental data analyzed in this study.We thank M.J.Reece for stimulating discussions on disordered materials and their attributes.The authors acknowledge funding received by The Institution of Engineering and Technology(IET)under the AF Harvey Research Prize.This work is supported in part by Engineering and Physical Sciences Research Council(EPSRC)ANIMATE grant(No.EP/R035393/1).
