Many a middle school science teacher has dripped a few drops of potassium iodide into hydrogen peroxide and watched the delight of their students as a volcano of foam erupted from the container. This experiment is often the way young people first learn about catalysts as something that that can induce a chemical reaction. But catalysts can make more than foam. As those young people grow into young scientists, they learn that catalysis--the acceleration of a chemical reaction by a catalyst--is a key process in the creation of just about everything. From the plastics that make up our medical equipment, to the gasoline in our cars, to the paint that colors our homes--none of these could exist without catalysts.

Machine learning is beginning to make a large impact in catalysis research, according to Bryan Goldsmith, Jacques Esterhuizen, and Jin-Xun Liu of the Univ. of Michigan, Christopher Bartel of the Univ. of Colorado Boulder, and Christopher Sutton of the Fritz Haber Institute of the Max Planck Society in their July AIChE Journal Perspective article, "Machine Learning for Heterogeneous Catalyst Design and Discovery." Novel catalysts are crucial for several applications, such as energy generation and storage, sustainable chemical production, and pollution mitigation. The current trial-and-error approaches to new catalyst discovery and synthesis are expensive and time-consuming. As an alternative, machine learning can be used to identify the top catalyst candidates before experimental testing, thereby accelerating catalyst discovery and design. Goldsmith and colleagues highlight several examples where machine learning is making an impact on heterogeneous catalysis research, such as: accelerating the determination of catalyst active sites and catalyst screening; finding descriptors and patterns in catalysis data; determining interatomic potentials for catalyst simulation; and discovering and analyzing catalytic mechanisms.