Harnessing Transfer Deep Learning Framework for the Investigation of Transition Metal Perovskite Oxides with Advanced...

Gas sensing materials based on transition metal perovskite oxides (TMPOs) have garnered extensive attention across various fields such as air quality control, environmental monitoring, healthcare, and national defense security. To overcome challenges encountered in traditional research, a deep learning framework combining natural language processing technology (Word2Vec) and crystal graph convolutional neural network (CGCNN) was adopted in this study, proposing a predictive method that incorporates a comprehensive data set consisting of 1.2 million literature abstracts and 110,000 crystal structure data entries. This method assessed the optimal combination of zinc-cobalt bimetallic ions complexed with ligands as precursors for perovskite oxides. The regulatory function of ligand concentration on the p-n transformation of zinc-cobalt oxide sensing performance was identified, and optimization strategies were provided. The Zn(II)/Co(III)/1-methyl-1H-imidazole-2-carboxylic acid complex was synthesized and demonstrated exceptional sensitivity and selectivity toward volatile organic compounds (VOCs), particularly 3-hydroxy-2-butanone (3H-2B). The p-n transformation mechanism of sensing performance was deeply analyzed through the construction of the hyper-synergistic ligand interaction matrix model for n-type sensors (HSLIM-n) and the parametrized surface-ligand resonance model for p-type sensors (PSLRM-p), enhancing the fundamental understanding of the sensing material properties. Even in highly interfering environments, the functionalized perovskite oxides exhibited outstanding sensitivity and selectivity toward 3H-2B gas, with a low detection limit of 25 parts per billion (ppb). This comprehensive research approach has facilitated the construction of a transfer learning-enhanced deep learning framework, which has shown high efficiency in predicting the performance and precise design of perovskite oxides, and its effectiveness was meticulously verified through detailed experimental validation.