Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling...

Area of Science:

• Biomedical Engineering
• Materials Science
• Computational Modeling

Background:

• Composite biomaterials are crucial for implants and scaffolds, requiring multi-scale mechanical and biological optimization.
• Natural tissue architectures inspire synthetic composite design for enhanced stiffness, toughness, and damage tolerance.

Purpose of the Study:

• To outline a data-driven, multiscale design paradigm for composite biomaterials.
• To integrate experimental data, imaging, and computational modeling for predictive engineering.

Main Methods:

• Multiscale mechanical and physicochemical characterization (e.g., nanoindentation, DMA, X-ray micro-CT).
• Image-based finite element models, continuum/mesoscale frameworks, and multiscale simulations.
• Machine learning approaches for structure-property relationships and generative design.

Main Results:

• Demonstrated linking of local microstructure to macroscopic performance (mechanics, transport, degradation).
• Illustrated acceleration of scaffold optimization via integrated experiment-model pipelines.
• Highlighted applications in orthopaedics, cardiovascular, soft tissue, regenerative medicine, and drug delivery.

Conclusions:

• A shift from trial-and-error to predictive, data-driven engineering of composite biomaterials.
• Future directions include digital twins and virtual patients for personalized therapies.
• Key challenges involve data quality, model interpretability, and bridging biological time scales.