| Abstract Scope |
After billions of years of evolution, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and manufacturing processes to create the next generation of high-performance engineering and biological materials by harnessing techniques integrating artificial intelligence, multiphysics modeling, and multiscale experimental characterization. This work combines computational methods and algorithms to investigate design principles and mechanisms embedded in materials with superior properties, including bioinspired materials. Additionally, we develop and implement deep learning algorithms to detect and resolve problems in current additive manufacturing technologies, allowing for automated quality assessment and the creation of functional and reliable structural materials. These advances will find applications in robotic devices, energy storage technologies, orthopedic implants, among many others. |