Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies

The explosive increase in popularity and increased accessibility to artificial intelligence and data science tools have opened the door for exploring previously deemed “too large” experimental spaces efficiently in experimental chemistry. This is of special interest for the field of electrocatalysis, where the development of new materials has relied largely on trial-and-error approaches that are not time- and resource-efficient. By leveraging these approaches, we can more effectively find promising electrocatalyst compositions and synthesis conditions that result in lower overpotentials and higher electrocatalyst durability. In this Technical Note, we use oxygen evolution electrocatalysts as a model to highlight potential pitfalls in the use of data-driven strategies for electrocatalyst development, focusing on the important choice of optimization metrics, normalization of data, and common errors that may appear during these kinds of approaches.