Machine learning dramatically streamlines search for more efficient chemical reactions

Even a simple chemical reaction can be surprisingly complicated. That's especially true for reactions involving catalysts, which speed up the chemistry that makes fuel, fertilizer and other industrial goods. In theory, a catalytic reaction may follow thousands of possible paths, and it can take years to identify which one it actually takes so scientists can tweak it and make it more efficient. Now researchers at the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have taken a big step toward cutting through this thicket of possibilities. They used machine learning – a form of artificial intelligence – to prune away the least likely reaction paths, so they can concentrate their analysis on the few that remain and save a lot of time and effort.