X-ray data and machine learning reveal catalytic changes

Direct observation of chemical reactions is notoriously difficult. Reaction rates tend to be too fast for chemists to be able to see how molecules move as they combine and change, and individual electrons — the species that are directly involved with reactions— are subject to the laws of quantum mechanics that make direct observation of their position impossible.

It’d especially difficult to observe reactions between organic molecules involving catalysts, because the reactions can take place at extreme temperatures and pressure, often proceed via very short-lived and unstable intermediates formed by combinations of the reactants with the catalyst. This makes it difficult to determine the mechanism of the reaction, which in turn complicates the design of improved catalysts.

An interdisciplinary team of chemists, physicists and computer scientists at the US Department of Energy's Brookhaven National Laboratory in New York State and nearby Stony Brook University have devised a method to analyse data from X-ray crystallography to decipher the three-dimensional nanostructures that form during catalysed reactions. The method relies on neural networks and machine learning to study previously-inaccessible information.