  Isolating the features associated with different materials growth conditions
is important to facilitate the tuning of these conditions for effective
materials growth and characterization. This study presents machine learning
models for classifying atomic force microscopy (AFM) images of thin film
MoS$_2$ based on their growth temperatures. By employing nine different
algorithms and leveraging transfer learning through a pretrained ResNet model,
we identify an effective approach for accurately discerning the characteristics
related to growth temperature within the AFM micrographs. Robust models with up
to 70% test accuracies were obtained, with the best performing algorithm being
an end-to-end ResNet fine-tuned on our image domain. Class activation maps and
occlusion attribution reveal that crystal quality and domain boundaries play
crucial roles in classification, with models exhibiting the ability to identify
latent features beyond human visual perception. Overall, the models
demonstrated high accuracy in identifying thin films grown at different
temperatures despite limited and imbalanced training data as well as variation
in growth parameters besides temperature, showing that our models and training
protocols are suitable for this and similar predictive tasks for accelerated 2D
materials characterization.
