Design and Synthesis of Electrocatalysts Base on Catalysis‐Unit Engineering

Abstract It is a pressing need to develop new energy materials to address the existing energy crisis. However, screening optimal targets out of thousands of material candidates remains a great challenge. Herein, an alternative concept for highly effective materials screening based on dual‐atom salphen catalysis units, is proposed and validated. Such an approach simplifies the design of catalytic materials and reforms the trial‐and‐error experimental model into a building‐blocks‐assembly like process. First, density functional theory (DFT) calculations are performed on a series of potential catalysis units that are possible to synthesize. Then, machine learning (ML) is employed to define the structure‐performance relationship and acquire chemical insights. Afterward, the projected catalysis units are integrated into covalent organic frameworks (COFs) to validate the concept Electrochemical tests confirming that Ni‐SalphenCOF and Co‐SalphenCOF are promising conductive agent‐free oxygen evolution reaction (OER) catalysts. This work provides a fast‐tracked strategy for the design and development of functional materials, which serves as a potentially workable framework for seamlessly integrating DFT calculations, ML, and experimental approaches.