Zhen Li, Clemson University
Zhaoxu Meng, Clemson University
Jiaoyan Li, The State University of New York at Buffalo
Understanding and predicting the advanced functionality of soft matter and hierarchical materials hold the key to solving some of today’s most pressing societal challenges, from sustainable energy storage to understanding biology and disease. Explaining the emergent behavior of soft matter and hierarchical materials, which arises from the complex interplay between structural morphology, architecture, interfaces, and chemical composition across time and length scales, leads to the rapid development of multiscale and multi-physics approaches in recent years.
This symposium calls for interdisciplinary research on soft matter and hierarchical materials ranging from engineered to natural and living systems that display multiscale features where nano-to-macro scales and hierarchical structures play key roles in the unique properties. We are interested in a variety of material systems, including but not limited to structural/infrastructural (e.g., cementitious, bituminous, clay), polymeric (e.g., nanocomposites, thin films, supramolecular networks), and biological and bioinspired (e.g., bone, wood, elastic tissue) materials. We are also interested in a wide range of approaches, including the mathematical theory of coarse-graining and model reduction, scale-bridging methods applied to soft matter and hierarchical materials, data-driven and machine-learning approaches, as well as innovative methods for characterizing and designing multi-functional soft matter and hierarchical materials.
This symposium will bring together researchers from a broad range of disciplines, including material science, mathematics, physics, chemistry, engineering, and computational science, to share insights and discuss the state-of-the-art methods and applications in the emerging field of multiscale materials modeling.
Topics (but are not limited to)
1. Mathematical theory of coarse-graining and model reduction.
2. Computational foundation of coupling heterogeneous physical models across scales.
3. Multiscale computational methods: physical models for different scales, coarse-graining strategies, concurrent coupling algorithms, data-driven multiscale models, machine learning models for multiscale material modeling.
4. Multiphysics coupled deformations of soft matter and hierarchical materials, multifunctional materials, and stimuli-responsive materials.