2020
DOI: 10.1016/j.advwatres.2020.103787
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DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials

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Cited by 73 publications
(44 citation statements)
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“…Finally, they tested the PiNN library on QM9 dataset containing 50,604 organic molecules and predicted properties like internal energy U 0 , and partial charges with very low MAE values [127,128]. In 2020 itself, Rabbani and coworkers published a similar deep learning workflow DeePore for characterising porous materials [129]. The model was developed on feed-forward Convolutional neural network (CNN) and uses 30 descriptors such as pore density, tortuosity, average coordination number, average pore radius, pore sphericity, etc.…”
Section: First-order Descriptor-based Modelsmentioning
confidence: 99%
“…Finally, they tested the PiNN library on QM9 dataset containing 50,604 organic molecules and predicted properties like internal energy U 0 , and partial charges with very low MAE values [127,128]. In 2020 itself, Rabbani and coworkers published a similar deep learning workflow DeePore for characterising porous materials [129]. The model was developed on feed-forward Convolutional neural network (CNN) and uses 30 descriptors such as pore density, tortuosity, average coordination number, average pore radius, pore sphericity, etc.…”
Section: First-order Descriptor-based Modelsmentioning
confidence: 99%
“…CNNs can be classified as a deep and complex version of ANNs, in which a series of matrix manipulations and multiplications transform an input tensor of data into a target tensor (Valavi et al, 2018). Considering the high potentials of CNNs in data transformation they have been frequently used for property estimation purposes in the porous material research (Alqahtani et al, 2020;Kamrava et al, 2020;Rabbani et al, 2020;Wu et al, 2019). As an example, effective diffusivity of the porous material as a macroscopic property which is an important parameter for reaction and catalysis modeling, can be obtained using CNN models (Wu et al, 2019).…”
Section: Convolutional Neural Network (Cnns)mentioning
confidence: 99%
“…Therefore, the supreme ability of CNNs to extract the rock morphological characteristics and relate to a physical property of a porous medium was corroborated, thereby a significant computational saving is envisaged. As another attempt to tackle deep characterization of porous material, a CNN-powered workflow is presented by Rabbani et al (2020) as DeePore. The model benefits from a large dataset of semi-realistic pore-scale images with 17,700 samples and 30 features which 15 of them are 1D vectors instead of scalar properties.…”
Section: Convolutional Neural Network (Cnns)mentioning
confidence: 99%
“…Nevertheless, the prediction of scaffolds’ mechanical performance directly from the CAD models, especially for cases in which FEM cannot be applied, may require advanced ML tools capable of using more complete descriptions of these complex geometries as input, and not just representative parameters. Quite recently, 2D convolutional neural networks (CNNs), with images as input, have been effectively applied to predicting multiple properties of porous materials, which constitutes a fundamental advance [ 21 ].…”
Section: Introductionmentioning
confidence: 99%