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RESEARCH PAPER · Journal of Materials Chemistry A · 2024

Leveraging machine learning in porous media

Mostafa Delpisheh, Benyamin Ebrahimpour, Abolfazl Fattahi, Majid Siavashi, Hamed Mir, Hossein Mashhadimoslem, Mohammad Ali Abdol, Mina Ghorbani, Javad Shokri, Daniel Niblett, Khabat Khosravi, Shayan Rahimi, Seyed Mojtaba Alirahmi, Haoshui Yu, Ali Elkamel, Vahid Niasar, Mohamed Mamlouk

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Leveraging machine learning in porous mediaOriginalExperimental performance analysis of methanol adsorption in granular activated carbon packed bed through design of a double pipe heat exchanger with longitudinal fins17The role of voronoi catalytic porous foam in reactive flow for hydrogen production through steam methane reforming (SMR): A pore-scale investigation19Machine learning for the advancement of membrane science and technology: A critical review107Development of the CO 2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms28Scaling the predictions of multiphase flow through porous media using operator learning16Advancing sustainable energy solutions with microfluidic porous media18Artificial Intelligence for Carbon Capture, Utilization and Storage: Technological Evolution and Systemic Integration Pathways2Deep learning spectral infrared digital holography for phase analysis of shale characterization4Experiment-in-loop interactive optimization of polymer composites for “5G-and-beyond” communication technologies6Hybrid mechanics-informed machine learning models for predicting mechanical failure in graphene sponge: a low-data strategy for mechanical engineering applications0Leveraging the Capabilities of Physics-Informed Neural Networks for Channel Optimization in PEM Fuel Cells0Lignin Surface Area, Pores, and Surface Pore Distribution0Lignin Surface Area, Pores, and Surface Pore Distribution0Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials30AI-Driven Inverse Design of Materials: Past, Present, and Future78Machine Learning-Based Model for Prediction Permeability in Porous Media: Method and Application to Unconventional Reservoirs2Thermal Transport in Porous Structures: Mechanisms, Modeling Approaches, and Future Directions3Research Progress on Micro/Nanopore Flow Behavior21A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Optimisation and characterisation of zinc chloride activated Saccharum spontaneum biochar for enhanced thermal energy storage efficiency using response surface methodology5AI-powered optimization and numerical techniques for nanofluid heat transfer systems-a review11Wasserstein Generative Adversarial Network for estimating capillary pressure in tight gas formations0Microstructure modulation strategies from pitch molecules to derived carbon materials for electrochemical energy storage5Novel Deep Learning Knowledge-Driven Supervised Backpropagated Recurrent Neural Networks for MHD Maxwell Hybrid Nanofluidic Model11Review of Artificial Intelligence Applications in Performance Prediction of Advanced Energy Materials2Performance comparison study of three ensemble learning models for predicting spatial apparent resistivity distribution around a cemetery site: A case study of Ejigbo cemetery in Osun State, Nigeria0Performance analysis of polymer electrolyte membrane fuel cells: Insights from sensor-based monitoring and machine learning5Review of machine learning application in porous transport layers for the design of fuel cells and electrolyzers6Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques4Investigating the corrosion transition mechanisms of zirconium alloys and the potential applications of machine learning in predictive modeling3Machine learning (ML)-assisted development of 2D green catalysts to support sustainability3Determining the macro mechanical properties of basalt from pore characteristics and meso-mechanical parameters of grain interfaces4Machine learning-driven porosity prediction: A case study from the Cretaceous Mata Series, Canterbury Basin, New Zealand2Engineering of Stabilized Ru‐Based Proton Exchange Membrane Water Electrolysis26Unraveling CO2 capture mechanisms in porous carbons across different pressure ranges: A synergistic approach combining machine learning and molecular simulation10Robust Human Activity Classification Using Ensemble and Linear Learning Models on Wearable Sensor Data0Advances in the Structural Design of Covalent Organic Frameworks for Direct Air Capture of CO 26Integrating lattice Boltzmann and neural networks for modeling transport parameters in porous rocks: A systematic review3Lab-on-a-chip insights: advancing subsurface flow applications in carbon management and hydrogen storage6A New Paradigm for Physics-Informed AI-Driven Reservoir Research: From Multiscale Characterization to Intelligent Seepage Simulation5Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Recent trends in porous materials science doped with MXene: toward AI-assisted design and performance optimization10Synthetic geological porous media generation using deep learning: a comprehensive review0Identification and monitoring platform for methanol adulteration in alcoholic beverages using porous silicon photonic structures: Possible implementation of AI platform0Exploring evaporation dynamics and brine concentration analysis of novel Photothermal materials in solar-driven desalination through experimental and computational fluid dynamics framework1Application of artificial intelligence in predicting and optimizing material properties3Advances in porous media for solar thermal energy with nanofluid enhanced volumetric receivers and non-Newtonian flow dynamics3Leveraging the capabilities of physics-informed neural networks for channel optimization in PEM fuel cells6Sustainable Enhanced Oil Recovery(EOR): From Laboratory Coreflooding to AI-Driven Field Deployment2A hybrid deep boosting framework with adaptive label stabilization for SEM-based porosity estimation in fly-ash cement mortar0Artificial intelligence–driven evolution and system integration for geological carbon capture, utilization, and storage0Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications3When Zeolites Meet Electrochemical Devices: Progress of Separators6AI enabled geospatial intelligence for the energy transition: A comparative CNN study on CCUS, geothermal siting, and NetZero strategies1From data to physics: a hybrid ANN–GA framework capturing dependencies in adsorption cooling and desalination systems2Co-designing collectors, electrolytes, and electrodes for sustainable batteries: a roadmap of techno-economics, circularity, and machine learning1Efficient Prediction Method for Methane Adsorption Properties of Covalent Organic Frameworks via Fusion of Graph Embedding and Global Features0Advanced algorithms for accurate mean pore size prediction in dolomite: Leveraging experimental petrophysical data0Advanced Machine Learning Models for Predicting Diffusion of Pollution in Soils0Integrated design strategies for high-performance supercapacitors: a comprehensive review on the role of current collectors, electrode materials, electrolytes, and AI-driven optimization5Machine learning assisted prediction of the compressive response of porous metallic bio-metamaterials2Identification of well logging data lithology based on physics-informed graph attention network2Recent Advances in CaO/Ca(OH) 2 Thermochemical Energy Storage: Materials, Reactors, and System Integration0Artificial intelligence and machine learning models for electrolyzers and fuel cells optimization: A review on empowering sustainable hydrogen economy2Comparative Machine Learning Framework for Permeability Prediction in a Heterogeneous Carbonate Reservoir0The AI Revolution in Carbon Capture, Utilization, and Storage1A review of the use of machine learning to predict the properties of porous media0Dual High-Field NMR Approach to Oil–Water Quantification in Porous Rocks0Hybrid finite element-deep learning method for deformation modeling of porous structures1Uninterruptible Power Supply (UPS) Battery Life Prediction Modeling Using Random Forest Algorithm and ANFIS0Machine learning enhanced data assimilation framework for multi-scale carbonate rock characterization0Explainable AI for subsurface carbon capture, utilization, and storage systems: A review1UAMF-GFPINN: Uncertainty-aware multi-fidelity gated fusion physics-informed neural network for heat transfer prediction in porous media0Data-Driven Modeling of Hysteresis in Porous Media: Neural-Network Surrogates Coupled with the OPM Flow Reservoir Simulator0Deep learning and multi-view image fusion for identification of karst voids and areal porosity quantification in deep carbonate reservoirs: a case study of the Lungu-7 block, Tarim Oilfield, China0Anisotropic permeability tensor prediction from porous media microstructure: A physics-informed MaxViT CNN-Transformer framework0Deep Learning for Geological Characterization and Modeling from Pore Scale to Reservoir Scale: State of the Art and Future Directions1Physics‐Driven 3D Structural Prediction and Transport Kinetics of Porous Carbons via Random Field and Phase‐Field Evolution0Electrical Conductivity Characterization in Shaly Reservoir Rocks Through Measurements and Optimization Techniques0Current status and future trends in anode porous transport layers for high-efficiency PEM water electrolysis0Study on draw resistance of tobacco-shred packed rods based on structural parameters using machine learning0Feature and Similarity-Based Explainability of Experimental Materials Data via Black-Box Hyperparameter Optimization0AI‐enabled Prediction and Inverse Design of Micro‐Nano Scale Convective Heat and Mass Transfer Using Physics‐informed Neural Networks Integrated with High‐Fidelity Nanofluid CFD Simulations0Future research directions in sustainable hydrogen: Production, storage, and utilization0Thermal-hydraulic characteristics of a high-efficiency zirconium heat exchanger: a multiscale CFD and porous-media study0Synergistic Ion-Bridging and Disjoining Pressure in Smart Brine EOR: A Hybrid Molecular Dynamics and Machine Learning Approach0A Monge–Ellipse Descriptor for Interpretable Quantification of Pore Architecture in Rock Images0CO2 Mineralization Reaction-Induced Pore-Structure Evolution and Its Effects on CO2 Sweep Behavior in Shale: Stage-Dependent Causal Discovery and Causality-Informed Machine Learning Assisted by LBM Simulations0

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  2. The role of voronoi catalytic porous foam in reactive flow for hydrogen production through steam methane reforming (SMR): A pore-scale investigation

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  4. Development of the CO 2 Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms

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