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

作     者：Achintha Ihalage Yang Hao 

作者机构：School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2021年第7卷第1期

页      面：659-670页

核心收录：

学科分类：08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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) 

主　　题：perovskite crystal disordered 

摘      要：Compositional disorder induces myriad captivating phenomena in ***-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by ***,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) *** phenomenon can be capitalized to predict the crystal symmetry of experimental compositions,outperforming several supervised machine learning(ML)*** 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 *** 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 *** concept further provides insights on possible phase transitions and computational modelling of complex *** proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.