Quantitative analysis of MoS2 thin film micrographs with machine learning

• Published in: Materials characterization Vol. 209
• Main Authors: Moses, Isaiah A., Reinhart, Wesley F.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2024
• Subjects: [formula omitted] thin film; Explainable AI; Machine learning; Morphological features; Transfer learning; Morphological features; Explainable AI; MoS2 thin film; Transfer learning; Machine learning
• ISSN: 1044-5803; 1873-4189
• DOI: 10.1016/j.matchar.2024.113701

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 MoS2 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 test accuracies of up to 70% 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 that humans could potentially miss. 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. •machine learning classification of MoS2 sample based on their growth temperatures.•morphological features associated with temperature variations identified.•the crystal quality and domain boundaries played crucial roles in classification.•models exhibit the ability to identify latent features that humans could potentially miss.