(GR4) The Path Towards Fossil Fuel Disruption: Predicting Biofuel Costs with a Single Experiment and Thirty Seconds

Our current response to climate change has been through broad-spectrum electrification, as seen in electric vehicles, through the use of energy storage technology. However, to enable the long-distance travel required for freighting and aviation, the energy density of hydrocarbon fuels have yet to be beaten. We can leverage organic wet wastes to produce renewable, low carbon intensity biofuels using hydrothermal liquefaction (HTL).

(GR5) Machine Learning for Materials Informatics

MXenes are a hot topic in materials science research because of their expected unique properties and myriad applications, such as more efficient energy conversion in batteries and solar cells, environmental and water treatment, and many additional applications. This project aims to produce Machine Learning (ML) models that accurately predict certain MXene properties – like electrical conductivity, work function, carrier density, mobility, life-time, and sensitivity to disorder – based on standard elemental information (e.g., electronegativity of each constituent element of the MXene, atomic mass of a MXene molecule, etc.), with training data found from literature as well as data produced by our project’s Density Functional Theory (DFT) team.