Materials Informatics: Data- and Knowledge-Driven Materials Science
Today, many critical technological applications, including energy, electronics, security and environment, that drive the modern world rely on the design and discovery of advanced materials. Typically, these materials are multicomponent by design and have enormous complexities at the atomic and mesoscale level. Therefore, predictive computational strategies that identify promising candidates with desired response for experimental synthesis and characterization has the potential to accelerate the discovery and realization of new materials at scale. Our interest is to build a transformative materials-by-design research program that leverages the state-of-the-art computational and experimental infrastructure to address grand challenge problems, including (but not limited to) clean energy and additive manufacturing, enabling key technological breakthroughs to foster new materials innovation. In the process, it is envisioned that our work will lay the foundation for an information science driven materials design and discovery approach that takes into account the existing empirical data (big or small), physical models, inference methods and computer simulation tools in pursuit of accelerating the design and discovery of new materials. Currently our three major research thrust areas are density functional theory, machine learning, and optimal learning.