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

Figure 1. Preparation, characterization, and data processing of gradient-thickness SRO films. (A) Schematic diagram of the preparation and characterization process for gradient-thickness SRO films; (B) Original AFM surface morphology of gradient-thickness SRO films; (C and D) Morphologies after (C) automatic preprocessing and (D) processing with different data augmentation strategies (RT-GeomAug, DBC-Aug, and OS-Aug). SRO: SrRuO3; AFM: atomic force microscopy; RT-GeomAug: real-time geometric augmentation; DBC-Aug: dynamic balance cropping augmentation; OS-Aug: over-sampling augmentation.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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