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 materials candidates remains a great challenge. Herein, we propose and validate an alternative concept for highly effective materials screening based on dual‐atom salphen catalysis units. Such an approach simplifies the design of catalytic materials and reforms the trial‐and‐error experimental model into a building‐blocks‐assembly like process. Firstly, density functional theory (DFT) calculations were performed on a series of potential catalysis units which were possible to synthesize. Then, machine learning (ML) was employed to define the structure‐performance relationship and acquire chemical insights. Afterwards, the projected catalysis units were integrated into covalent organic frameworks (COFs) to validate the concept Electrochemical tests confirm that Ni‐SalphenCOF and Co‐SalphenCOF are promising conductive agent‐free oxygen evolution reaction (OER) catalysts. This work provides a fast‐tracked strategy for design and development of functional materials, which serves as a potentially workable framework for seamlessly integrating DFT calculations, ML, and experimental approaches.This article is protected by copyright. All rights reserved