Chemical and Physical Properties of Hybrid Nanoparticles
Hybrid nanoparticles are composed of inorganic metallic core with either an organic or metal-organic outer shell. This along with their (sub)nanometer size, has promoted their exploration as components of nanoelectronics, biosensing schemes, photochemical applications, and as electrocatalysts. Understanding these systems can lead to advances in photochemical, electrochemical, and environmental technologies.
Modeling Electrochemical and Heterogeneous Catalysis
Catalysis is key in a variety of industrial, biological, environmental, and technological fields. Being able to predict and design materials that can outperform previous surfaces and devices is critical for improving catalytic technologies. At the core of developing future materials, one must understand not only the reaction networks, but also the role of substrate morphology and electronic structure. We are interested in understanding the mechanistic details of various reduction and oxidation reactions on various metal-oxide and nanoscale organometallic structures using computational approaches.
Machine Learning for Molecular Materials
Determining structure and properties using ab initio methods is a mainstay in chemistry. Often researchers need to understand the electron response using time-intensive quantum theories and computational techniques beyond traditional DFT approaches to design molecular materials. We are using machine learning to accelerate the discovery of organic molecules with precise electron response properties for batteries and quantum materials.