| Abstract Scope |
In this talk we review how modeling, experiment and synthesis are integrated to understand, design and leverage novel smart material manufacturing for advanced mechanical properties through the use of physics-based generative AI. This allows us to mimic and improve upon natural processes by which materials evolve, and how they meet changing functional needs. Applied specifically to protein materials, this integrated materiomic approach is revolutionizing the way we design and use materials, and has the potential to impact many industries, as we harness data-driven modeling and manufacturing across domains and applications. The talk will cover several case studies covering distinct scales, from silk, to collagen, to biomineralized materials, as well as applications to food and agriculture. A specific focus will be on the use of multi-agent transformer-based attention models as foundational theories, ultimately applied to solve multi-modal material modeling, design and analysis problems. |