Structure–property relationship of metal–organic frameworks for alcohol-based adsorption-driven heat pumps <i>via</i> high-throughput computational screening

Li, Wei; Xia, Xiaoxiao; Cao, Meng; Li, Song

doi:10.1039/c8ta07909a

Cited by 59 publications

(58 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Screening potential MOFs from a vast number of MOF databases for adsorption-driven heat pumps and chillers has been extensively investigated in recent decades ( Liu et al., 2020 ; Shi et al., 2020 ). A high-throughput computational screening of MOFs for alcohol-based adsorption-driven heat pumps based on grand canonical Monte Carlo has been conducted in our previous study ( Li et al., 2019a ), from which the correlation between MOF structure property and their coefficient of performance (COP), as well as the top performers with the highest COP, were identified. A computational screening of 2930 MOFs for adsorption-driven heat pumps and chillers has also reported, and six structures with the highest working capacities were obtained ( Erdős et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

Summary 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to its characteristics of high porosity, large specific surface area and adjustable structure, it is regarded as a candidate for traditional adsorption. Although MOFs have long been used in the fields of gas adsorption [ 8 ], separation [ 9 , 10 ], storage [ 11 , 12 ], catalysis [ 13 ], and heat pumps [ 14 , 15 ], the use of MOFs to harvest water vapor from the atmosphere has only been proposed in recent years [ 1 , 16 , 17 ]. This is because structural stability of MOFs may deteriorate after absorbing water.…”

Section: Inductionmentioning

confidence: 99%

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Shi

Liang

et al. 2022

Nanomaterials

View full text Add to dashboard Cite

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.

show abstract

“…In a recent study, Li et al evaluated ethanol as a working fluid for alcohol‐based adsorption driven heat pumps. [ 351 ] This was done by using GCMC integrated with standard thermodynamic equations to screen ≈2900 MOFs with high‐quality ab initio derived point charges from the CoREMOF [ 311 ] database. A three‐tiered screening process was deployed, systematically increasing the number of MC steps in each simulation round, and simulating: evaporation, condensation, and desorption.…”

Section: Computational Screening Of Metal–organic Frameworkmentioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

View full text Add to dashboard Cite

Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

show abstract

Structure–property relationship of metal–organic frameworks for alcohol-based adsorption-driven heat pumps via high-throughput computational screening

Abstract: Adsorption-driven heat pumps (AHPs) based on metal–organic frameworks (MOFs) have been garnering rapidly growing research interests due to their outstanding adsorption performance.

Cited by 59 publications

References 40 publications

Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Contact Info

Product

Resources

About