A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials

Fanourgakis, George S.; Gkagkas, Konstantinos; Tylianakis, Emmanuel; Froudakis, George E.

doi:10.1021/acs.jpcc.9b10766

Cited by 69 publications

(55 citation statements)

References 23 publications

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“…For instance, the RF algorithm was utilized to predict the CO 2 , H 2 , and H 2 S adsorption capacities of 2932 CoRE MOFs using structural properties of MOFs along with a new descriptor, the probability of a set of different probe atoms to be adsorbed by the material. 140 Three different probe atoms were modeled for adsorption probability calculations: Vprobes (neutral, nonpolar probes), Dprobes (neutral, small dipole moment), and Qprobes (small charge in the center). The number of MOFs used in the training set was varied from 50 to 1000 while the rest of the MOFs were used for the validation set.…”

Section: Gas Storage Performances Of Mofsmentioning

confidence: 99%

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Altıntaş

Altundal

Keskın

et al. 2021

J. Chem. Inf. Model.

148

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The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure–performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

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Section: Gas Storage Performances Of Mofsmentioning

confidence: 99%

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Altıntaş

Altundal

Keskın

et al. 2021

J. Chem. Inf. Model.

148

View full text Add to dashboard Cite

show abstract

“…In fact, HTCS includes Monte Carlo simulations (MC) and machine learning (ML) [ 20 ], and previous studies on HTCS have usually relied on molecular simulation. In recent years, ML has been widely applied to various fields, including image recognition [ 21 ], natural language processing [ 22 ], data classification and mining [ 23 , 24 ], and material performance prediction [ 25 ]. In our previous work, Shi et al [ 26 ] combined MS and ML to screen MOFs with good performance that can be used for adsorption heat pumps.…”

Section: Inductionmentioning

confidence: 99%

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

Shi

Liang

et al. 2022

Nanomaterials

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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.

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“…Fitting of batch sorption performance data and characteristics of sorbent materials into machine learning models could rapidly attest the practicability of the sorbent materials based on the type of treated water systems that could promise higher success rate of upscaling the laboratory technology for field operations. [ 78 ]…”

Section: Reality Challenges and Opportunitiesmentioning

confidence: 99%

Graphene‐Based Sorbents for Multipollutants Removal in Water: A Review of Recent Progress

Yap

Nine

Hassan

et al. 2020

Adv Funct Materials

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The coexistence of multiple toxic water pollutants (heavy metals, organic dyes, oils, and organic solvents) limits the sustainable supply of clean water worldwide and urges the development of advanced water purification technology that can remove these contaminants simultaneously. Since its discovery, graphene‐based materials have gained substantial attention toward development of new‐generation sorbents for water purification. Despite several recently published reviews on water purification technology using graphene and its derivatives, there is still a gap in the review considering multiple water‐pollutant remediation using advanced graphene materials. In this review, in the first instance, a comparative structure–function–performance relationship between graphene‐based sorbents and the multipollutants in water is established. A fundamental correlation is made between the sorption performance for diverse pollutants in water with the more specific adsorption properties (surface area, pore size, type of functional groups, C/O, C/N, and C/S atomic ratio) of advanced graphene sorbents. Second, the underlying interaction mechanisms are uncovered between different classes of water pollutants using single graphene‐based sorbents. Third, the rational design of advanced multipollutant sorbents based on graphene is elaborated. The reality, challenges, and opportunities of advanced graphene materials as emerging sorbents for sustainable water purification technology are finally presented in the last section.

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A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials

Cited by 69 publications

References 23 publications

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

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

Graphene‐Based Sorbents for Multipollutants Removal in Water: A Review of Recent Progress

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