Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications

Abstract

Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO₂ capture, CH₄ storage, gas separation, catalysis, etc. Traditional methods of material research, which mainly rely on manual experimentation, are not particularly efficient, while with advancements in computer science, high-throughput computational screening methods based on molecular simulation have become crucial in material discovery, yet they face limitations in terms of computational resources and time. Currently, machine learning (ML) has emerged as a transformative tool in many fields, capable of analyzing large data sets, identifying underlying patterns, and predicting material performance efficiently and accurately. This approach, termed “materials genomics”, combines high-throughput computational screening with ML to predict and design high-performance materials, significantly speeding up the discovery process compared to traditional methods. This review discusses the functions of ML in the screening, design, and performance prediction of COFs and highlights their applications across various domains like CO₂ capture, CH₄ storage, gas separation, and catalysis, thereby providing new research directions and enhancing the understanding of COF materials and their applications.