Inverse Design of Metal‐Organic Frameworks for CH₄/N₂ Separation Enabled by Coupled Machine Learning and Genetic Algorithms

Metal‐organic frameworks (MOFs) have emerged as promising candidates for gas separation, yet the vastness of their structural design space renders experimental screening prohibitively time‐ and resource‐intensive. Recent advances in machine learning (ML) technology offer powerful alternatives for accelerating MOF discovery through data‐driven prediction. In this work, a high‐accuracy ML model with a Tangent Adaptive Genetic Algorithm (TAGA) is integrated to enable inverse design of MOFs for CH 4 /N 2 separation. The ML model, trained on structural features including topology, metal/organic secondary building units, and functional groups, is embedded within the TAGA framework to efficiently navigate the high‐dimensional chemical space. Analysis of the evolutionary trajectory reveals that MOFs featuring the fsc topology and ligands such as pyrene, anthracene, and naphthalene consistently exhibit superior CH 4 /N 2 selectivity. Based on these high‐performance genotypes, a series of MOF structures are constructed, among which the top‐performing candidate achieves an IAST selectivity of 15.92 and a CH 4 uptake of 2.47 mmol g −1 . This study highlights a paradigm shift from trial‐and‐error screening toward goal‐directed materials design, offering a generalizable pathway for developing next‐generation separation materials.