  Deep learning models are widely used for the data-driven design of materials
based on atomic force microscopy (AFM) and other scanning probe microscopy.
These tools enhance efficiency in inverse design and characterization of
materials. However, limited and imbalanced experimental materials data
typically available is a major challenge. Also important is the need to
interpret trained models, which have typically been 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. Our models show accurate
predictions in five classes of transition metal dichalcogenides (TMDs)
(MoS$_2$, WS$_2$, WSe$_2$, MoSe$_2$, and Mo-WSe$_2$) 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, DoG 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.
