The complex behavior of polymeric materials emerges from a delicate interplay between molecular architecture, thermodynamics, and external stimuli, making their design and prediction a formidable challenge. Recent advances in polymer informatics, molecular simulation, and artificial intelligence (AI) offer a transformative approach to understanding and engineering polymers across scales.
Traditionally, polymer physics has relied on experimental observation and theoretical modeling to unravel the complex behaviors of polymeric materials. However, the integration of data-driven methods, high-throughput simulations, and machine learning techniques now offers powerful new tools for addressing longstanding challenges. This talk will explore how these emerging technologies are being applied to unlock molecular-level mechanisms, predict macroscopic properties, and accelerate materials discovery.
I will discuss recent developments in building polymer-specific databases and descriptors, the use of multiscale simulations to bridge time and length scales, and AI strategies—such as predictive modeling and generative design—for enhancing structure-property relationships. Special emphasis will be placed on how these approaches complement traditional experiments and theories, yielding fresh insights into phase behavior, mechanical performance, transport phenomena, and dynamic responses in polymer systems.
Finally, I will highlight current limitations, open research questions, and opportunities for collaborative efforts in integrating experiments, informatics, simulation, and AI into the broader field of polymer physics.