Grant: Discovery Grants Program - Individual

Complex material systems, such as hybrids, alloys, and composites, are transforming modern technological landscapes, ranging from aerospace engineering to the biomedical device industry. The applications are endless: novel alloys with higher strength and lower density open unlimited opportunities to enhance the reliability of reusable aerospace vehicles. Small-scale soft robots using magnetic soft matter are establishing new frontiers in targeted drug delivery and minimally invasive surgery. The current material design approach relies heavily on trial and error, which is inefficient and ineffective due to the large number of design parameters and the lack of understanding about material uncertainties and multiscale structure-function relationships. To fill these knowledge gaps and increase the design efficiency of complex material systems, my research team will develop a novel physics-consistent data-driven material design paradigm. The key novelty of our design paradigm is its integration of fundamental physics with modern data science. It will model the behaviors of complex material systems through a multiscale, multiphysics computational framework and leverage data mining and machine learning techniques for efficient material design and optimization. Specifically, we will construct accurate and robust computational techniques to numerically generate physics-consistent data complementary to scarce experimental data. We will devise effective homogenization schemes to connect material macroscopic properties with microscopic structures and mechanisms. Combining the above numerical strategies with machine learning techniques, we will reveal hidden material multiscale structure-function relationships that are otherwise difficult to discover. We will also systematically quantify material uncertainties originating from their random microstructures and fabrication processes, while identifying the material design parameters that optimize targeted performances. The physics-consistent data-driven material design paradigm represents a general methodology for material modeling, characterization, and discovery, which can be applied to various complex material systems and facilitate material innovations. These innovations will further drive technological transformations in medical devices, flexible electronics, and soft robotics, generating significant social, environmental, and economic benefits. Moreover, our research will advance the theories and techniques in scale-bridging, uncertainty quantification, and high-dimensional constrained optimization, which are fundamental research questions faced by many scientific disciplines. Located at the intersection of solid mechanics, numerical methods, materials science, and scientific computing, this research will impart participating students with key skills, such as theoretical derivation and computational programming, to succeed in the interdisciplinary research and development landscape of the future.