Data‐Driven Design of Metal–Organic Frameworks for Efficient Coalbed Methane Purification: From Molecular Fingerprint Mining to Experimental Validation

The separation of methane (CH 4 ) and nitrogen (N 2 ) from coalbed methane (CBM) using metal–organic frameworks (MOFs) is of significant importance. However, the similar physicochemical properties of the CH 4 and N 2 coupled with the vast structural diversity of MOFs, render the conventional trial‐and‐error to discovering of high‐performance materials particularly difficult. To dresses this challenge, herein, we utilize a data‐driven strategy that integrates machine learning (ML) with high‐throughput computational screening (HTCS) to facilitate the rational design of novel MOFs for CH 4 /N 2 separation. By mining experimental databases and conducting probabilistic modelling, extracting the ligand fragments and metal centres associated with positive molecular fingerprint features. Finally, fingerprint‐positive fragments were assembled into novel ligands and experimentally synthesized with corresponding metals to form a new MOF (Ni‐BIC). The synergistic effect of pore size and pore environment enables Ni‐BIC to exhibit high CH 4 /N 2 adsorption ratio (6.4) and outstanding CH 4 /N 2 (50/50, v/v) selectivity (13.1) at 298 K and 1 bar. Breakthrough experiments reveal exceptional CH 4 /N 2 separation performance, with a dynamic CH 4 capacity of 9.0 cm 3 g −1 combined with excellent recyclability. This study has successfully implemented a complete workflow encompassing fragment mining, reverse design, and experimental synthesis, providing guidance for the targeted development of novel high‐performance MOFs.