Machine Learning in Electrocatalysis

ACS In Focus Machine Learning in Electrocatalysis is designed for newcomers, breaking down complex ideas into approachable concepts and offering practical examples to help readers understand how these technologies can be used in real-world research and applications. This primer does not aim to be an exhaustive technical manual; rather, it aims to provide accessible, actionable knowledge. The authors avoid overly complex mathematical formulas and instead focus on explaining concepts clearly with practical, relatable examples from electrocatalysis.

The authors recommend approaching this primer by following the chapters to build understanding progressively, especially if new to ML or electrocatalysis. If you are already familiar with one of these fields, jump ahead to the most relevant chapter. Along the way, keep the following guiding questions in mind: How can ML be used to optimize catalytic processes? What tools and methodologies can streamline material screening and synthesis?