Abstract
Computational approaches to describe catalysts under electrochemical conditions are steadily increasing. Yet, particularly the theoretical description of nanostructured catalysts, which have the advantage of a high surface area or unique electronic properties through refined synthetic protocols, is still hampered by the occurrence of pitfalls that need to be circumvented. In this perspective, we aim to introduce the reader to common pitfalls in the modeling of nanostructured catalysts with applications in energy conversion and storage, and we discuss the application of machine learning techniques as a potential solution to overcome the associated gaps.
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