How to Efficiently Design 2D Materials for Electrochemical Applications Using Machine Learning

Two dimensional (2D) materials have transitioned from lab findings to potential applications. Starting with the isolation of graphene, the field has rapidly expanded to encompass a broad spectrum of materials, including transition metal dichalcogenides, MXenes, and so on. Each of them offers unique structural, electronic, optical, and electrochemical properties. These materials have been recognized as candidates for applications in energy storage and conversion including electrocatalysts. As we approach the limits of traditional "trial-and-error" methods, the integration of statistical analysis, machine learning (ML), live (real-time) electrochemistry, and generative AI presents a compelling path forward. These tools are no longer aspirational; they are becoming essential to navigating the vast and complex design space of 2D materials for electrochemical applications in the future.