A universal machine learning framework to automatically identify high‐performance covalent organic framework membranes for CH₄/H₂ separation

Abstract

A universal machine learning framework is proposed to predict and classify membrane performance efficiently and accurately, achieved by combining classical density functional theory and string method. Through application of this framework, we conducted high‐throughput computations under industrial conditions, utilizing an extensive database containing nearly 70,000 covalent organic framework (COF) structures for CH4/H2 separation. The best‐performing COF identified surpasses the materials reported in the previously documented MOF and COF databases, exhibiting an impressive adsorption selectivity for CH4/H2 exceeding 82 and a membrane selectivity reaching as high as 248. More impressively, some of the best candidates identified from this framework have been verified through previous experimental works. Furthermore, the automated machine learning framework and its corresponding scoring system not only enable rapid identification of promising membrane materials from a vast material space but also contribute to a comprehensive understanding of the governing mechanisms that determine separation performance.