CEE Seminar: Data-Driven Computational Design of Emerging Microstructural Material Systems
Design of material systems with complex microstructures represents the future of materials development to achieve unprecedented product performance. While most of the existing methods are trial-and-error based, we are proposing data-driven systematic computational design methods that provide a seamless integration of design optimization, predictive materials modeling, processing/manufacturing, and data/informatics to enable the accelerated design and development of advanced materials systems. In this talk, we will introduce the state-of-the-art computational design methods for designing heterogeneous nano- and microstructural materials systems such as polymer nanocomposite, nanodielectric polymers, light-weight composite structures, and thin-film solar cells. Research developments in microstructure characterization and reconstruction, deep machine learning of key microstructure features, data-driven Bayesian optimization for mixed variables, and multiscale uncertainty quantification will be introduced. Challenges and opportunities in designing engineered material systems will be discussed.