Interpretable Machine Learning To Accelerate Nanocatalyst Discovery

U-M researchers have developed a framework that can rapidly pinpoint which materials keep platinum nanocatalysts from breaking down at high temperatures, a crucial step for cleaner fuel and chemical production. The full article explains how they uncovered insights that would have taken traditional simulations months to reveal and what this could mean for the future of fuel production. Read more to see how their findings could accelerate the path toward more efficient, sustainable fuels.