Work in this research thrust includes using computational tools to tackle design of materials in complex combinatorial search spaces, such as organic electronic materials and energy storage polymers. In addition to screening large numbers of possible candidates, machine learning tools are used to address the inverse design question, that is, given the desired properties, imagine the material. Additional work in this area involves applying and developing revolutionary microscopy techniques to connect the atomic structure and chemistry of defects/interfaces with material properties for applications including quantum computing, energy storage, power electronics, and dielectrics.
