Large Language Model-Assisted Systematic Study of Anomalous Adsorption Phenomena

Abstract

Soft porous crystals can undergo guest-induced structural transitions producing unconventional adsorption isotherms, which are valuable for gas separation and storage applications. However, the evidence for these transitions is scattered across the literature in narrative form and has not been aggregated. Here we use a large language model pipeline to mine the literature for anomalous adsorption, extracting phase-transition events together with material, adsorbate, temperature and pressure metadata and classifying each into an established behavioural class. Benchmarked against human annotation, the extraction stage attains an F1 score of 0.96. When applied at scale, the pipeline yields a curated corpus of 547 transitions across 240 distinct materials, drawn from 200 of 4,252 screened publications, which we further link to crystallographic structures. Analysis of this database shows that the principal behavioural classes, gate-opening and breathing, are distinguished by their functional response. Gate-opening frameworks transform at a significantly lower pressure than breathing frameworks (p = 3.8E-3). In contrast, no difference in pore size, void fraction, linker chemistry or metal identity was detected between the classes. Our findings argue for a functional rather than purely structural description of responsive porous materials. They also highlight the need for a community database that records the guest, temperature and pressure of every reported transition of these materials.