(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.

(GR7) Gravity-Driven Multiple Effect Thermal System (G-METS) Distillation for Efficient Low-Cost Magnesium Refining

The process of multiple effect distillation for the recycling of magnesium can both increase efficiency and reduce cost by up to 90% when compared to batch distillation refinement. This presentation will detail goals and applications of a novel continuous gravity-driven multiple effect thermal system (G-METS) distillation process for magnesium alloys.

(GR9) Rare Earth Metal Recycling Using a Novel, Low-cost Distillation Technology

We are perfecting a technology that will extract rare earth metals from magnet scrap because rare earth metals are in short supply in the United States. 95% of rare earth metal production is carried out in China, and right now, there are no U.S. producers. The only non-Chinese producers are Estonia, Vietnam, and Thailand- a small market.

We are looking to build a start-up in the U.S. to fill the vacuum, and part of our research is to prove that out.