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

ABSTRACT: 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 CH₄/N₂ 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 traject

SUBMITTER: Li W 

PROVIDER: S-EPMC12697905 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature