Exploring high-performing adsorption-driven heat pumps (AHPs) remains a challenging task owing to the low working capacity, high regeneration temperature, and low energy efficiency of conventional adsorbents. Quick discovery of the novel promising adsorbents could help to improve the coefficient of performance of AHPs for heating (COPH) and cooling (COPC). Herein, we reported an approach to identify the high-performing covalent-organic frameworks (COFs) for heating, cooling, and ice making by high-throughput computational screening based on grand canonical Monte Carlo simulations and, for the first time, machine learning. It was demonstrated that compared with metal-organic frameworks (MOFs), COFs were more suitable adsorbents of AHPs for cooling because of their weak interaction toward ethanol that favors stepwise adsorption. Structure-property relationship analysis revealed that the average enthalpy of adsorption commensurate with the enthalpy of evaporation will benefit the performance of AHPs besides the high working capacity and low step positions of adsorption isotherms. In order to reduce the computational cost of screening, a random forest model was developed to successfully predict the COPC of both COFs and MOFs.
Keywords: adsorption heat pumps; coefficient of performance; covalent−organic frameworks; high-throughput computational screening; machine learning.