Data-Driven/Machine Learning-Enabled Design for Nanocomposites

Machine learning and data analytics have emerged in recent years as new paradigms to accelerate the explorations of functional materials. This session invites all research studies to the fields of functional nanocomposite design accelerated by multiscale materials modeling and simulation tools as well as machine learning algorithms. With data-driven guidance, theoretical, computational, and experimental explorations in polymer-, oxide-, and metal-based hybrid materials are welcome, with foci on mechanical, electrochemical, electrical, photonic, or functional properties. New applications in membranes, batteries, soft materials, robotics, sensors, biomaterials, among others, are encouraged.