Machine learning-driven morphology identification and classification of high-throughput functional oxide films

Functional oxide films offer precise control over diverse properties through tunable physical characteristics and interface effects, with their functionality primarily determined by morphology. However, conventional methods are incapable of obtaining large-scale morphological data and face significant challenges in data identification and classification, which fundamentally limit the rapid assessment of thin film properties and functional screening. Herein, we establish a comprehensive morphological database of oxide films utilizing high-throughput experimental methods and develop a machine learning framework for automated identification and classification of atomic force microscopy data. Using gradient-thickness SrRuO3 films as a representative example, this framework achieves enhanced performance through hyperparameter optimization and strategic adjustments, ultimately reaching a classification accuracy of 86.67% in independent tests, demonstrating its effectiveness in morphology analysis of functional oxide films. Furthermore, this approach shows significant potential for automated microstructure analysis of complex oxides and is expected to accelerate research on structure-property correlations in functional oxide films.