Research
We are a computational research group using machine-learning and molecular simulation to tackle key challenges in chemical engineering and materials design for applications in energy storage, catalysis, and desalination.
ML potential development
Molecular simulation is a powerful tool for predicting properties of materials and fluids, but the reliability of these predictions heavily depends on the models used. The challenge in developing these models is a trade-off between the degree of chemical detail (level of theory used) and their computational cost (how fast they can be evaluated on supercomputers). Our group is tackling this challenge by leveraging machine-learning frameworks trained on quantum mechanical datasets to build models that can predict large-scale thermodynamic properties at first-principles levels of accuracy.
Fluids at interfaces and in confinement
How molecules behave at an interface or in confinement is vastly different from how they behave in bulk. These types of environments are where analytical theories tend to break down, where timescale trends diverge, and where chemical reactions are more likely to happen. Our group seeks to unravel the intricate relationship between surface features, geometries, and the subsequent influence on fluid behavior. Ultimately, we are interested in uniquely designing surfaces toward target fluid properties for separations processes and catalysis.
Generative design for ion transport
A key goal in designing energy storage solutions is to identify solvents which enhance charge transport and stability. Our group leverages machine-learning tools to map molecular structures to transport properties and optimize for desired properties in lower dimensional spaces. This approach allows us to greatly accelerate the exploration and discovery of electrolytes for energy storage applications.
