| Abstract Scope |
Rapid advances in computing and machine learning, along with developments in materials processing that mimic unique features and characteristics found in natural systems, offer unprecedented opportunities to design and deploy a new generation of engineered and biological materials. We demonstrate specific examples of materials design, analysis, or characterization using machine learning and biomimetics from the vantage point of three different disciplines: materials science, plant science, and biomedical science. The examples chosen here cover a spectrum of topics that include: modulating the bandgap of natural and engineered materials for applications in microelectronics, optoelectronics and energy systems; characterization of material properties at multiple length scales using multi-fidelity approaches in machine learning; design of new classes of plant-based materials with unique properties for environmental sustainability, soft robotics and flexible electronics; and machine-learning approaches combined with microfluidics and computational simulations to assess, monitor and guide clinical outcomes in such diseases as diabetic retinopathy. |