The rapidly evolving landscape of polymer science increasingly depends on the integration of theoretical frameworks, computational methods, and artificial intelligence. This session explores how these complementary approaches are transforming our ability to understand, predict, and design polymeric materials with unprecedented precision and efficiency.
Traditional theoretical polymer physics continues to yield essential insights into fundamental phenomena, from chain conformations and phase transitions to dynamics and self-assembly. Computational methods now bridge multiple length and time scales, providing atomistic detail through molecular dynamics, mesoscopic understanding via field theories, and macroscopic predictions via multiscale modeling. The recent rise of AI and machine learning adds transformative capabilities: accelerating computational workflows, revealing hidden patterns in complex data, and enabling inverse design strategies that were once intractable.
This session welcomes contributions across the full spectrum—from fundamental theoretical advances and innovative computational techniques to groundbreaking AI applications and their synergistic combinations. We seek work that pushes methodological boundaries while maintaining scientific rigor, whether through standalone breakthroughs in individual domains or integrative approaches that leverage multiple methodologies. By fostering dialogue among theorists, computational scientists, and AI researchers, we aim to advance both fundamental understanding and practical capabilities in polymer science.