Development of a machine learning computational technique for estimation of molecular diffusivity of nonelectrolyte organic molecules in aqueous media

Aregawi, Beyene Hagos; Majed, Rafael; Su, Chia‐Hung; El-Shafay, A.S.; Alashwal, May; Felemban, Bassem F.; Zwawi, Mohammed; Algarni, Mohammed; Wang, Fuming

doi:10.1016/j.molliq.2022.118763

Cited by 2 publications

(1 citation statement)

References 76 publications

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“…Burner et al used the atomic property-weighted radial distribution function (AP-RDF), the bag-of-atoms, and the chemical motif density descriptors on a wide range of MOF structures to model CO 2 working capacity and CO 2 /N 2 selectivity . It is interesting to notice an increasing interest in the development of models to characterize the dynamic behavior of adsorbed molecules for membrane design. − We can mention some works on ion-exchanged zeolites and flexible MOF materials (particularly ZIFs) devoted to the diffusion mechanism of small gases, which has kinetic diameters that are equal to or larger than the actual window size. , The role played by extra-framework ions of zeolites in the diffusion of small gases such as CO 2 may include cooperative effects − or gate-opening mechanisms that are too complex and difficult to reproduce using simple topological descriptors. Recently, van den Bergh used DFT calculations to explain the anomalous diffusion behavior behind the alkane/alkene mixture selectivity of ZIF-7 …”

Section: Classification Of Porous Materials Descriptorsmentioning

confidence: 99%

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

Nieto-Draghi,

Creton,

Martin

et al. 2024

ACS Appl. Eng. Mater.

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The generalization of high-throughput synthesis has recently allowed the discovery of thousands of new porous materials, generating a large amount of information, with the development of specialized databases. Widespread access to databases enabled an increase in algorithms and models for property prediction and in silico design of materials. The structural information on materials still needs to be rationalized by the inclusion of descriptors to ease the characterization of solids. This is essential for in silico screening to potential applications based on machine learning (ML) approaches. Indeed, at the forefront of a real revolution in the selection and design of porous materials for many industrial applications, the use of appropriate descriptors to encode solid material properties (topology, porosity, and surface chemistry) is one of the fundamental aspects of the development of MLbased models. Our analysis of the literature reveals a lack of descriptors based on the potential energy surface (PES) of crystalline materials embedding crucial information such as the porosity, the topology, and the surface chemistry. In this work, we introduce new PESbased descriptors including the surface probability distribution of the local mean curvature (K H ), the electrostatic-PES distribution (σ e ), as well as the local electrostatic-potential gradient surface probability distribution (∇σ e ). Our descriptors allow the classification of zeolites as well as its characterization by self-containing standard morphological and topological information (pore diameter, tortuosity, surface chemistry, etc.). We illustrate their usage to generate accurate ML-based models of the isosteric heat of adsorption of CO 2 on purely siliceous zeolites of the IZA database and ion-exchanged zeolites in the function of the Si/Al ratio for the case of LTA topology.

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Section: Classification Of Porous Materials Descriptorsmentioning

confidence: 99%

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

Nieto-Draghi,

Creton,

Martin

et al. 2024

ACS Appl. Eng. Mater.

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Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Shdefat,

Mostafa,

Al-Arnaout

et al. 2024

Int J Comput Intell Syst

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This research addresses the accuracy issues in IoT-based human activity recognition (HAR) applications, essential for health monitoring, elderly care, gait analysis, security, and Industry 5.0. This study uses 12 machine learning approaches, split equally between support vector machine (SVM) and k-nearest neighbor (k-NN) models. Data from 102 individuals, aged 18–43, were used to train and test these models. The researchers aimed to detect twelve daily activities, such as sitting, walking, and cycling. Results showed k-NN models achieved slightly higher accuracy (97.08%) compared to SVM models (95.88%), though SVM had faster processing times. The improved machine learning approaches proved effective in accurately classifying daily activities, with k-NN models outperforming SVM models marginally. The paper provides significant contributions to the field of HAR by enhancing the performance of SVM and k-NN classifiers, optimizing them for higher accuracy and faster processing. Through robust testing with samples of real-world data, the study provides a detailed comparative analysis that highlights strengths and weaknesses of each classifier model, specifically within IoT-based systems. This work not only advances the theoretical understanding and practical applications of HAR systems in areas, such as healthcare and industrial automation, but also sets the stage for future research that could explore hybrid models or further enhancements, consequently improving the efficiency and functionality of IoT devices based on activity recognition.

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Development of a machine learning computational technique for estimation of molecular diffusivity of nonelectrolyte organic molecules in aqueous media

Cited by 2 publications

References 76 publications

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

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Development of a machine learning computational technique for estimation of molecular diffusivity of nonelectrolyte organic molecules in aqueous media

Cited by 2 publications

References 76 publications

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO2 Gas Adsorption into Zeolites

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO2 Gas Adsorption into Zeolites

Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers

Contact Info

Product

Resources

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Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites

Potential Energy Surface-Based Descriptors for Nanoporous Materials and its Applications to Classification and CO₂ Gas Adsorption into Zeolites