Deep Learning-Assisted Elucidation of Structure-Performance Relationships in MOF-NH 3 Adsorption Refrigeration Working Pairs

= 0.880 ± 0.030), as its local receptive field generates MOFs feature fingerprints to capture the deep characteristics of the structure-performance relationships. Subsequently, via the dependence analysis of SHAP values, the MOFs parameters exerting the most significant impacts on adsorption refrigeration performance were ranked as volume-related parameters, surface area-related parameters, and pore size-related parameters. Notably, among the volume-related parameters, the specific pore volume exhibited the best predictive performance, as it captures both the steric effect and adsorption potential energy of MOFs. Furthermore, the optimal ranges of each parameter that contributes most to refrigeration performance were quantified. This study deepens the understanding of MOFs-ammonia adsorption mechanisms and provides guidance for the design of next-generation carbon-neutral materials in the field of adsorption refrigeration.