Machine learning in polymer science: A new lens for physical and chemical exploration

Polymers, as foundational materials in modern industry, face persistent challenges in precision design and performance improvement due to structural intricacy, multifunctionality requirements, and sustainability imperatives. Machine learning (ML) has emerged as a transformative tool for elucidating structure–property correlations and expediting polymer material discovery. This review systematically examines ML applications across three domains: autonomous synthesis via reaction kinetic modeling, cross-scale property prediction linking polymeric configurations to bulk behavior, and sustainability-driven design frameworks. For automation synthesis, ML integrates polymerization kinetics with structure control and polymerization efficiency, enabling closed-loop systems for autonomous process refinement. In performance prediction, ML deciphers hierarchical architectures relationships with thermal resilience, optoelectronic responses, and mechanical robustness, providing physicochemical theory frameworks for tailored material design. Critical analyses address persistent limitations, including data paucity in specialty polymer classes, interpretability deficits in multimodal architectures, and validation gaps between simulation and experiments. By synergizing generative algorithms with high throughput experimentation, this strategy transcends empirical trial-and-error approaches, establishing a computational design paradigm spanning molecular-to-bulk scales. The resultant synergy between computational intelligence and polymer science not only streamlines material discovery cycles but also unlocks sustainable solutions for energy storage, eco-friendly materials, and adaptive smart systems, heralding a new era of data-driven macromolecular engineering.