Understanding and predicting the advanced functionality of soft matter and hierarchical materials holds the key to solve some of the most pressing societal challenges, from sustainable energy storage to understanding biology and diseases. Explaining the emergent behavior of hierarchical materials, arising from the complex interplay between structural morphology, architecture, interfaces, and chemical composition across time and length scales, calls for a multiscale and multiphysics approach. To that end, a diverse set of computational modeling and theoretical tools have been developed at the interface of physics, chemistry, mechanics and material science. In recent years, artificial intelligence-based data-driven approaches have added to the arsenal of tools to tackle complex challenges related to the mechanics of such materials. In particular, integration of AI with multiscale modeling has opened a new avenue to improve our fundamental understanding of the structure and mechanical properties of the soft material and advance the material designs for a broad array of biomedical and engineering applications.
A group of active researchers in the area of multiscale modeling and artificial intelligence in soft matter biophysics have formed a consortium and organized the Weekly Online Complex Fluids and Soft Matter (CFSM) Seminar Series (
cecas.clemson.edu/zhenli/cfsm) to promote the interaction between researchers across the globe. This Research Topic aims to build on this seminar series and provide a great opportunity to convert the online interaction to offline collaboration. Specifically, this collection intends to create collaborative opportunities to boost direct communications between scientists and engineers from different backgrounds, including multiscale theories, computational approaches, multiscale physical models across scales, and diverse engineering applications. This Topic also calls for interdisciplinary research on soft matter and hierarchical materials ranging from engineered to natural and living systems that display nano, micro and macro-scale features. This Research Topic will bring together international researchers from a broad variety of mathematics, physics, computational science, engineering, and chemistry disciplines to share insights on multiscale methods and artificial intelligence for soft matter and multicomponent materials and to discuss the state-of-the-art in the emerging fields of multiscale modeling and artificial intelligence
We are interested in a variety of material systems including but not limited to the biophysical studies of polymeric (e.g., nanocomposites, thin films, supramolecular networks), and biological and bioinspired (e.g., filaments, membrane, cells, soft tissue) materials. We welcome review articles and perspectives in addition to original research.
Specific topics welcome in this collection include 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 for multiscale modeling
4. Multiphysically coupled deformations of soft matter and multicomponent materials, multifunctional materials, and stimuli-responsive materials
The authors of published paper in this issue will be invited for presentation at the CFSM Seminar Series seminar series for a broader impact. The editor of this special issue Dr. Lu Lu, will provide instructions on how to use DeepXDE (https://deepxde.readthedocs.io/en/latest/), a library for scientific machine learning and physics-informed learning, to the potential authors who plan to employ machine learning approaches to their research but lack of technical tools.