Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials.

Area of Science:

• Materials Science
• Biomaterials Engineering
• Nanotechnology

Background:

• Biopolymers and bio-inspired materials possess hierarchical structures enabling remarkable toughness and damage tolerance.
• These materials leverage multiscale component assembly, from atomistic to macroscale, for advanced properties.

Purpose of the Study:

• To explore and summarize the functionality and mechanisms of biopolymers and bio-inspired materials across multiple length scales.
• To review modeling methods for biopolymer applications and highlight the role of artificial intelligence in material improvement.

Main Methods:

• Review of literature focusing on biopolymer nanofibril configurations, synthetic biopolymers, and bio-inspired composites.
• Analysis of theoretical modeling approaches at multiple length and time scales.
• Emphasis on artificial intelligence-driven methodologies for material characterization, fabrication, and design.

Main Results:

• Biopolymers and bio-inspired materials exhibit hierarchical organization crucial for material performance.
• Multiscale modeling provides a theoretical basis for understanding and predicting material behavior.
• Artificial intelligence offers pathways to enhance functionality, biodegradability, sustainability, and design of these materials.

Conclusions:

• Biopolymers and bio-inspired materials are versatile for creating advanced materials with enhanced properties.
• Future applications in manufacturing are promising, with potential for significant lifecycle impacts.
• Integration of AI is key to unlocking the full potential of these sustainable materials.