Computer learning technique will help design novel materials

A new method for identifying metallic catalysts for specific chemical conversions could help materials scientists and chemists design new materials with useful properties, predict the method’s inventors at Virginia Tech. The researchers, led by chemical engineers Luke Achenie and Hongliang Xin, used neural networks and machine learning techniques to ‘teach’ a computer system to study the interactions between important chemical groups on the surfaces of different metals to determine which alloys would be the best at catalysing the desired reactions.

Previously, catalyst identification has been a trial-and-error process which, although it has led to the discovery of polymers and other compounds with novel and useful properties, is very time-consuming and expensive. Catalytic activity depends on the arrangement of atoms on the surface of a metal and how, when organic compounds adsorb onto the surface, the metal can donate electrons to groups of atoms in the structure of the compounds, therefore allowing a reaction to proceed. The researchers describe their method in a paper in the Journal of Physical Chemistry Letters.