Executive Summary: Directly visualizing the trajectories of electronic and nuclear motion can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of electrons, hence the breaking and reforming of chemical bonds, can be best realized through a close coupling of theory and experiment. As a diverse group of computational chemists, computational materials scientists, and computer scientists, we work seamlessly with experimental groups that conduct state-of-art research in solid/liquid/gas interfacial as well as isolated systems for renewable energy applications.

Leveraging our expertise in the ab-initio predictions of imagining, spectroscopy, and performance data abundant in catalytic, electrocatalytic, and battery applications, our dream is to create a virtual universe that best resembles the physical reality, whose validity could then be tested macroscopically. In order to directly link the atomic picture in theory and the macroscopic observables in operando condition experiments (with a special focus on core-level spectroscopies), we develop and apply computational methods such as electronic structure theory, quantum mechanics, molecular dynamics, and kinetics modelling targeting at challenges across the time domain and the length domain. 

The group's current research interest is in developing a new suite of ab-initio/empirical hybrid tools called “Digital Twin”, a faithful digital copy of multi-modal characterization techniques, that is a) rooted in rigorous theoretical chemistry development at the most fundamental electronic structure level, b) enables coupling with the larger time/length scale of the chemical complexities in real, operando experiments, so that we could walk into future experiments with foresight versus hindsight. Initial success has been seen in X-ray based core-level spectroscopy.   

Welcome to the Qian Group!

Chemical Sciences Division, Lawrence Berkeley National Lab