Xin Qi

The emergence of modern hierarchical nanomaterial design, where the performance of higher-level structures can be controlled by modulating local interfacial properties and connections of lower-level building blocks, has tremendously accelerated the progress in energy-, electronic-, catalytic- and biomedicine-related applications. While numerous functional nanomaterials have been discovered by experiments over the last three decades, inverse design for optimized function, new properties, controlled syntheses and architecture, and mass manufacturing is still difficult to achieve. Our research focuses on developing and using multiscale theoretical frameworks to understand the underlying physics in observed events, including catalysis, shape-controlled synthesis, and self-assembly, and using the extracted principle to achieve simulation-guided functional nanomaterial design. We specialize in computational modeling, and we select and integrate the best methods to target our goals, spanning statistical mechanics, colloidal theory, atomistic and CG simulations, density functional theory, rare event sampling, and machine learning.