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Abstract Deep learning models based on atomic force microscopy enhance efficiency in inverse design and characterization of materials. However, the limited and imbalanced data of experimental materials that are typically available is a major challenge. Also important is the need to interpret trained models, which are normally complex enough to be uninterpretable by humans. Here, we present a systemic evaluation of transfer learning strategies to accommodate low-data scenarios in materials synthesis and a model latent feature analysis to draw connections to the human-interpretable characteristics of the samples. While we imagine this framework can be used in downstream analysis tasks such as quantitative characterization, we demonstrate the strategies on a multi-material classification task for which the ground truth labels are readily available. Our models show accurate predictions in five classes of transition metal dichalcogenides (TMDs) (MoS₂, WS₂, WSe₂, MoSe₂, and Mo-WSe₂) with up to 89% accuracy on held-out test samples. Analysis of the latent features reveals a correlation with physical characteristics such as grain density, Difference of Gaussian blob, and local variation. The transfer learning optimization modality and the exploration of the correlation between the latent and physical features provide important frameworks that can be applied to other classes of materials beyond TMDs to enhance the models’ performance and explainability which can accelerate the inverse design of materials for technological applications.