Performance Evaluation of Metal–Organic Frameworks in Adsorption Heat Pumps via Multiscale Modeling

The adsorption heat pump (AHP) driven by low-grade thermal energy is a promising technology to reduce building energy consumption for sustainable energy. Using metal–organic frameworks (MOFs) as adsorbents has attracted widespread attention in AHPs due to their large capacity of working fluids, a stepwise adsorption isotherm that tends to possess outstanding equilibrium performance (i.e., coefficient of performance, COP). Nevertheless, the dynamic performance of MOFs in AHPs lacks a quick evaluation and screening strategy, especially for specific cooling power (SCP) that is equally important with COP during operation. Herein, multiscale modeling combining the molecular simulation and the mathematical simulation of AHPs was proposed to obtain the SCP and COP for a vast number of MOF-based working pairs with high efficiency. Structure–property relationship obtained from the high-throughput computational screening of 1072 MOFs indicated that relatively low density (<1 kg/m3), large pore size (>10 Å), and a relatively high void fraction (∼0.6) benefited the improvement of working capacity (ΔW), leading to high performance eventually. From a dynamic perspective, it was also suggested that the adsorption/desorption of working fluids majorly occurring in the temperature ranges of 305–325 and 330–345 K was favorable for the MOFs to achieve better SCP and COP. Furthermore, the successful implementation of several commonly used machine learning (ML) algorithms paves the way for accelerating the assessment of the dynamic performance for nanoporous materials with reasonable computation time. During the training of ML algorithms, it was revealed that ΔW and transport diffusion were the dominant descriptors for predicting SCP, while equilibrium adsorption performance and MOF density played a vital role in predicting COP.