文章基本信息

标题：Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning
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作者：Rui Long ; Xiaoxiao Xia ; Yanan Zhao 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：1
页码：1-33
DOI：10.1016/j.isci.2020.101914
语种：English
出版社：Elsevier
摘要：SummaryAdsorption-driven osmotic heat engines offer an alternative way for harvesting low-grade waste heat below 80°C. In this study, we performed a high-throughput computational screening based on grand canonical Monte Carlo simulations to identify the high-performance metal-organic frameworks (MOFs) from 1322 computationally ready experimental MOF structures for adsorption-driven osmotic heat engines with LiCl-methanol as the working fluid. Structure-property relationship analysis reveals that MOFs exhibiting high energy efficiency possess large working capacity, pore size and surface area, and moderate adsorption enthalpy comparable to the evaporation enthalpy. Furthermore, machine learning is employed to accelerate the computational screening for satisfied MOFs via the structure properties. The optimal structure properties of the MOFs are further identified via the ensemble-based regression model by optimizing the energy efficiency via the genetic algorithm, which shed light on rationally designing and fabricating MOFs for desired heat-to-electricity conversion.Graphical abstractDisplay OmittedHighlights•Structure-property relationship of MOFs are revealed via GCMC•Qualified absorbents are of larger LCD and ASA and moderate adsorption enthalpy•Optimal MOF structure properties are obtained through machine learningOrganic Chemistry; Energy Resources; Energy Systems; Computational Materials Science