ArticleiScience2021
Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Article
- Interactions of Co, Cu, and non-metal phthalocyanines with external structures of SARS-CoV-2 using docking and molecular dynamics.Scientific reports · 2022Article
- Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting.Nanomaterials (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adsorption-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.
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