Computation-Guided Nanomaterial Design for
Energy, Sustainability, and Medicine
Our lab aims to combine simulation, theory, data science, and machine learning to expedite the discovery of nanoscale materials to address challenges in energy generation and storage, environmental crisis, and medicine. In particular, we focus on organic-inorganic solid-liquid interfaces where interfacial behaviors can be modulated by programmable molecular chemistry and ambient parameters. We build bottom-up theoretical frameworks to understand material properties and top-down frameworks to realize cost-effective, computation-guided inverse design.
Interface
Interfaces are where magic happens. They often act as knobs to tune the architecture and properties of hierarchical materials. We use ab initio methods, atomistic molecular dynamics simulations, and machine learning to unravel the complex phenomena at the multi-component solid-liquid interface.
Nanocrystal Growth and Assembly
Shape-controlled nanocrystal growth and their assembly into higher-level structures are governed by a convoluted interplay between macroscopic and molecular interactions. We combine theory and multi-scale simulations to extract the underlying energetics, predict local and mesoscale architectures and finely tailor the outcomes through inverse molecular design.
Polymer Upcycling
The demand to address plastic waste is pressing. Heterogeneous catalysis is a promising route to decompose plastic waste into industrial feedstock or value-added commercial products. Despite being widely used, the exact role of catalysts and reaction mechanisms are largely unclear and awaiting us to use computational tools to provide insight and guide further optimization.
Method Development
An appropriate methodology or framework may be missing on the path to achieving our goal. We are interested in developing new methods and suites based on statistical mechanics theory, data science, and computer science to close the gap.