Bridging Quantum Chemistry and Machine Learning for a Greener Future

At the crossroads of quantum chemistry and machine learning, researchers are advancing novel discoveries in materials science to improve energy efficiency—new knowledge that could revolutionize how we tackle global challenges like sustainability and energy production.

Phillip Pope, a sixth-year doctoral student in computer science at the University of Maryland, is active in this area, combining his skills in machine learning with a strong commitment to combating the climate crisis.

A native of St. Petersburg, Florida, Pope has seen firsthand the devastating impacts of climate change.

“My parents regularly face hurricanes, and the threat of intense weather is a very visceral experience for me,” he says. “I believe that climate change is the cause, and we need to do more as academics to help reduce our carbon footprint.”

Pope is currently working on developing methodologies to speed up algorithms in quantum chemistry. The vision of this work is to reduce reliance on fossil fuels, particularly through the discovery of catalysts, for example, to replace platinum in hydrogen fuel production.

Hydrogen fuel is produced by splitting water via electrolysis, a process that uses an electric current to separate water into hydrogen and oxygen. Platinum is typically used as a catalyst to speed up this process, but its scarcity and high cost limit the scalability of hydrogen production, Pope says.

“As it stands, there’s not enough platinum in the world to meet the projected hydrogen demands,” he says. “Wouldn’t it be great if we could find cheaper, more efficient catalysts to advance this technology?”

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