Machine learning cuts complexity of computational calculations in catalysis

In heterogeneous catalysis, calculations require complicated computational processes due to the variability of reaction pathways and possibilities. Now, thanks to a clever combination of programming and machine learning, researchers have achieved a dramatic increase in the speed of simulations, enhancing the energy efficiency of an otherwise resource-hungry process. The results, reported for reactions to convert carbon dioxide into fuels, could rapidly translate into other industrially relevant reactions, such as depolymerisation and biomass valorisation.