Using Molecular Modeling and Machine Learning to Develop Advanced Materials for Sustainability

Date
Mar 22, 2023, 4:00 pm5:00 pm

Speaker

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Event Description

Despite dramatic increases in the deployment of renewable energy technologies, greenhouse gas emissions have continued to climb. These emissions are largely structural, as was made clear during the pandemic when, despite a huge reduction in worldwide economic activity and travel, greenhouse gas emissions hardly changed. This makes clear that while human behavior is important, the systems that society has put in place are key to helping achieve worldwide sustainability goals. This means engineers play an essential role in meeting the environmental challenges we face in the future.

Technological advances are needed to facilitate the adoption of renewable energy, expand non-CO2 emitting energy sources such as nuclear power, and prevent emissions of other potent greenhouse gases such as hydrofluorocarbon refrigerants and methane. In this talk, I will highlight some of my group’s research efforts focused on the use of molecular modeling to understand and develop materials that can help realize these technologies. I will present three short vignettes on: 1) the discovery of efficient, safe, and Earth-abundant water-in-salt electrolytes that can be used in batteries coupled to wind and solar generation facilities; 2) fundamental studies on molten salts that can use used in next-generation nuclear reactors; and 3) how ionic liquids are being developed to recover and separate high global warming potential hydrofluorocarbon refrigerants. Finally, I will demonstrate how machine learning and optimization techniques can be used as a powerful companion to molecular simulation to speed the material discovery process.