Computational Catalysis and Machine Learning Applications to Water Treatment Technologies

Electrocatalysis can offer new routes to water treatment. For example, electrocatalytic reduction is an emerging technology for treating oxyanions of concern in water. However, identification of highly performant, cost-effective catalysts remains a major barrier to deployment at scale. This article discusses how computational modeling and machine learning can accelerate the search for new catalyst materials. It describes how traditional computational chemistry workflows, now deployed in their basic form for at least two decades, can be expanded in breadth and depth through newly developed machine-learned force fields that have been trained on millions of examples. It also discusses how the theory and machine learning pipeline can effectively integrate with experimental synthesis and characterization platforms to rapidly identify and validate new catalyst chemistries.