Deep Learning for Polymer Classification: Automating Categorization of Peptides, Plastics, and Oligosaccharides

Abstract

Polymers represent a diverse and vital class of materials across numerous industries, each with unique structural characteristics and functional properties. Traditional methods of polymer classification rely heavily on labor-intensive techniques prone to subjectivity and human error. The emergence of deep learning has significantly transformed material science by enabling automated analysis and classification of complex polymers. In this study, we focus on leveraging deep learning models to classify three distinct classes of polymers: peptides, plastics, and oligosaccharides. Peptides, plastics, and oligosaccharides represent significant subsets of the polymer family, each with distinct structural features and applications. Our research explores the effectiveness of various deep learning architectures, including deep learning to classify peptides, plastics, and oligosaccharides, achieving perfect accuracy with neural networks, K-Nearest Neighbors, and Random Forest classifiers. Principal Component Analysis enabled visualization of sample distribution, demonstrating deep learning's potential to automate and enhance polymer classification, reducing reliance on traditional, labor-intensive methods.