Finding high-performing catalysts, which are used to accelerate processes from chemical manufacturing to energy production, can be a slow, expensive process, often relying on years of trial-and-error or massive computational resources. To add to the difficulty, ideal catalyst candidates are rare. Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a new machine learning framework that can accelerate the search for better catalysts—the materials that speed up chemical reactions—and offer more reliable results.