Machine learning and in silico discovery of metal-organic frameworks: Methanol as a working fluid in adsorption-driven heat pumps and chillers

Shi, Zenan; Liang, Hong; Yang, Wenyuan; Liu, Jie; Liu, Zili; Qiao, Zhiwei

doi:10.1016/j.ces.2019.115430

Cited by 50 publications

(50 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 (Erd} os et al, 2018). Shi et al (Shi et al, 2020) conducted a high-throughput computational screening of 6013 computation-ready experimental MOFs to select the suitable methanol-MOF working pair for adsorption-driven heat pumps, and 30 MOFs were selected as promising candidates.…”

Section: Accessmentioning

confidence: 99%

“…Among these adsorbents, MOFs attracted considerable attentions because of their outstanding adsorption performance due to the high volumetric surface area, structural diversity, and structural tunability ( Li et al., 2016 , 2019b ; Altintas et al., 2018 ; Kirchon et al., 2018 ). 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.…”

Section: Introductionmentioning

confidence: 99%

“…Shi et al. ( Shi et al., 2020 ) conducted a high-throughput computational screening of 6013 computation-ready experimental MOFs to select the suitable methanol-MOF working pair for adsorption-driven heat pumps, and 30 MOFs were selected as promising candidates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

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: Accessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The models of 3 gases are shown in Figure S1, The atomic charge of MOF was estimated using the MOF electrostatic-potential-optimized charge scheme (MEPO-Qeq) method [42], which accurately evaluated electrostatic interactions. Due to the advantages of the MEPO-Qeq method with fast and accurate, it is widely used in various systems of adsorption-MOF [43][44][45]. The Lennard-Jones (LJ) electrostatic parameters were obtained from the universal force field (UFF) [46] and are listed in Table S1 Data from previous studies had shown that the UFF-TraPPE force field combination could accurately predict the adsorption and diffusion behaviors of these 3 gases in MOFs [40,47,48].…”

Section: Molecular Modelmentioning

confidence: 99%

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

Deng

Yang

et al. 2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.

show abstract

“…This makes it possible to relate input features to a MOF’s performance in a particular application. To do so effectively, one needs to find interpretable feature descriptors, whose values can be related to recognizable MOF properties 9 – 14 . Additionally, the diversity of properties and the vast number of structures makes it especially desirable to have an automatic framework to generate expressive features that work across multiple applications, enabling more transferability and less “handcrafting.”…”

Section: Introductionmentioning

confidence: 99%

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Krishnapriyan

Montoya²,

Harańczyk

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

show abstract

Machine learning and in silico discovery of metal-organic frameworks: Methanol as a working fluid in adsorption-driven heat pumps and chillers

Cited by 50 publications

References 53 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

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Contact Info

Product

Resources

About