| Scope |
Advances in computational modeling, data science, and machine learning are transforming the design of biomaterials, bio-related materials, bio-inspired materials, and sustainable materials. This symposium focuses on computational and data-driven approaches that enable predictive understanding and accelerated materials design, with particular emphasis on multiscale modeling and machine learning methods. Computational studies that are closely integrated with, informed by, or validated through experimental investigations are strongly encouraged, aiming to establish robust structure–property–processing relationships across multiple length and time scales and to advance the development of recyclable and reprocessable material systems.
Topics include, but are not limited to:
* Multiscale computational modeling of biological, bio-related, and bio-inspired materials
* Machine learning and AI approaches for materials design and property prediction
* Data-driven design of sustainable, recyclable, and reprocessable materials
* Computational studies of biopolymers and hybrid bio-synthetic materials
* Physics-informed machine learning and AI-accelerated materials modeling
* Integrated computational–experimental frameworks for bio-inspired materials design |