Machine Learning in the Macromolecular Sciences: From Methods to Applications. A Review

Machine learning (ML) and artificial intelligence are revolutionizing macromolecular and polymeric sciences. This review comprehensively explores ML methodologies, including supervised and unsupervised learning strategies, reinforcement and deep learning, generative and coarse-grained models, and their applications in predicting material properties and behaviors, optimizing synthesis, processes, and advancing polymer design and computational strategies. We focus on foundational studies, recent advancements, and future directions.