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

中文翻译：

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基于催化单元工程的电催化剂设计与合成

开发新能源材料是解决现有能源危机的迫切需要。然而，从数千种候选材料中筛选出最佳目标仍然是一个巨大的挑战。在此，我们提出并验证了基于双原子 Salphen 催化单元的高效材料筛选的替代概念。这种方法简化了催化材料的设计，并将试错实验模型改革为积木组装式的过程。首先，对一系列可以合成的潜在催化单元进行密度泛函理论（DFT）计算。然后，利用机器学习（ML）来定义结构-性能关系并获得化学见解。随后，将预计的催化单元集成到共价有机框架（COF）中以验证这一概念。电化学测试证实 Ni-SalphenCOF 和 Co-SalphenCOF 是有前途的无导电剂析氧反应（OER）催化剂。这项工作为功能材料的设计和开发提供了一种快速策略，可作为无缝集成 DFT 计算、ML 和实验方法的潜在可行框架。本文受版权保护。版权所有