Ensemble-Learning-Guided Optimization Design for Metal-Organic Framework Adsorbents toward CO Adsorption

Metal-organic frameworks (MOFs) hold great potential for carbon monoxide (CO) adsorption owing to their large pore volume, diverse periodic network structures, and designability. Machine learning is anticipated to provide optimization parameters for designing high-efficiency MOFs adsorbents, avoiding time-consuming experiments. Here, we proposed an ensemble-learning strategy accounting for multidimensional analysis of features to rationally design pore geometries, structural properties, and synthesis conditions of MOFs toward high performance for CO adsorption. The extreme gradient boosting model exhibited the best predictive performance (R² > 0.95) under limited data set size. Porous characteristic was identified as a dominant factor in pristine MOFs. Prediction results illustrated that MOFs featuring one-dimensional, two-dimensional, microporous, and isolated pores were optimal for CO adsorption, with 0.4-0.6 cm³/g total pore volume. This enhanced adsorption capacity can be attributed to the shortened molecular diffusion pathways. The relative significance of structural parameters followed: space groups > geometry > topology. The optimal structural configuration involved space group of R3m, binuclear paddle wheel geometry, and scorpionate-like topology. Regarding transition metal-modified MOFs, incorporated Cu(I) demonstrated the strongest binding affinity toward CO, while Fe(II) and Ni(II) could serve as effective binding sites. This work offers a theoretical guidance for designing efficient adsorbents toward CO adsorption.