Using Artificial Intelligence To Forecast Water Oxidation Catalysts

Artificial intelligence and various types of machine learning are of increasing interest not only in the natural sciences but also in a wide range of applied and engineering sciences. In this study, we rethink the view on combinatorial heterogeneous catalysis and combine machine learning methods with combinatorial approaches in electrocatalysis. Several machine learning methods were used to forecast water oxidation catalysts on the basis of literature published data sets and data from our own work. The machine learning models exhibit a decent prediction precision based on the data sets available and confirm that even simple models are suitable for good forecasts.