Research graph
References from Machine learning-guided design of engineered metal-organic frameworks for sustainable hydrogen storage and production. Local targets link to admitted publications; unresolved targets remain external evidence.
Global energy demand and the role of technological innovation towards renewable energy generation for economic growth and a cleaner environment: a review
10.1080/27658511.2026.2633481 · 2026 · External reference
Rational design of ligand-immobilized Rh/IRMOFs catalysts for 1-butene hydroformylation with high regioselectivity
2024 · External reference
Green hydrogen pathways for sustainable urban development: infrastructure, policy, and sociotechnical integration
10.1155/je/1096041 · 2026 · External reference
Life cycle greenhouse gas reduction in bioenergy with carbon capture and storage processes for green hydrogen production
10.1016/j.biortech.2026.134906 · 2026 · External reference
Integrated monitoring and lifecycle assessment of green hydrogen, ammonia, and synthetic fuels: advancing environmental sustainability and carbon traceability in the clean energy transition
10.1002/ep.70328 · 2026 · External reference
Hydrogen as an energy carrier: production pathways, thermochemical constraints, and electrolysis-based green hydrogen prospects
10.1016/j.ijhydene.2026.154100 · 2026 · External reference
Hydrogen: challenges and opportunities for storage and transportation in the clean energy economy
10.1002/tcr.202500218 · 2026 · External reference
Green hydrogen pathways for a net-zero future: technologies, circular economy integration, life-cycle performance and safety dimensions
10.1039/d5ra09599a · 2026 · External reference
Hydrogen energy as sustainable energy resource for carbon-neutrality realization
10.1021/acssusresmgt.4c00039 · 2024 · External reference
Overview of hydrogen production processes: health and environmental impact
10.1002/ep.70229 · 2026 · External reference
A life cycle assessment review of climate impacts from low-carbon hydrogen production technologies
10.1021/acs.energyfuels.5c05144 · 2026 · External reference
Industrial symbiosis concept applied to green hydrogen production: a critical review based on bibliometric analysis
10.1007/s43621-024-00780-8 · 2024 · External reference
Photothermally enhanced electrocatalytic water splitting for hydrogen production
2026 · External reference
Progress in green hydrogen production and innovative materials for fuel cells: a pathway towards sustainable energy solutions
10.1016/j.ijhydene.2024.09.153 · 2025 · External reference
Advancements in metal–organic frameworks (MOFs) for high-capacity hydrogen storage: mechanisms, optimization strategies, and future perspectives
10.1016/j.ijhydene.2026.154063 · 2026 · External reference
Machine learning assisted predictions for hydrogen storage in metal–organic frameworks
10.1016/j.ijhydene.2023.04.338 · 2023 · External reference
Synthesis and biomedical applications of highly porous metal–organic frameworks
10.3390/molecules27196585 · 2022 · External reference
Survival of zirconium-based metal–organic framework crystallinity at extreme pressures
10.1021/acs.inorgchem.2c04428 · 2023 · External reference
ZIF-8 membrane
2016 · External reference
Exploring MOF-74 composites: from novel synthesis to cutting-edge applications
10.1016/j.inoche.2025.114026 · 2025 · External reference
Synthesis methods, performance optimization, and application progress of metal–organic framework material MIL-101(Cr)
10.3390/chemistry7030078 · 2025 · External reference
Nanoarchitectonics of metal–organic framework and nanocellulose composites for multifunctional environmental remediation
10.1002/adma.202504364 · 2025 · External reference
Innovative routes for hydrogen production from waste plastics: a comprehensive review of thermochemical, photocatalytic, and electrocatalytic technologies
10.1039/d5gc04989j · 2026 · External reference
Recent advances in hydrogen production technologies: environmental and economic perspectives
10.1186/s44147-025-00853-z · 2026 · External reference
Next-generation photocatalysts for hydrogen production: innovations, challenges, and future perspectives
10.1021/acs.energyfuels.6c00604 · 2026 · External reference
Clean hydrogen in the energy transition: experimental assessment of purity profiles and environmental traces
2026 · External reference
Advancements in solar-powered hydrogen production: a review of concentrated solar power electrolysis, photoelectrochemical, and PV-based electrolysis
10.1007/s44373-025-00080-4 · 2025 · External reference
Advancements in catalytic technologies for chemical hydrogen storage: materials, mechanisms, and future prospects
10.1002/smll.202511191 · 2026 · External reference
Hydrogen fuel cell technologies for decarbonization: opportunities, challenges, and strategic pathways, a comprehensive review
10.1002/ese3.70411 · 2026 · External reference
Recent progress in catalysts for sustainable hydrogen production: a comprehensive review
10.1016/j.ccr.2025.217109 · 2026 · External reference
Engineering functional groups in UiO-66 metal-organic frameworks for hydrogen storage: a structure-property relationship investigation
10.1016/j.ijhydene.2025.152660 · 2026 · External reference
In-situ synthesized IRMOF-1/graphene oxide-carbon nanotubes hybrid for boosting electrochemical hydrogen storage
10.1016/j.jallcom.2026.187157 · 2026 · External reference
Metal–organic frameworks for photocatalytic hydrogen production coupled with selective oxidation reactions
10.1002/cphc.202500459 · 2025 · External reference
Porosity tunable metal-organic framework (MOF)-based composites for energy storage applications: recent progress
10.3390/polym17020130 · 2025 · External reference
Metal-organic frameworks for advancing photocatalytic and electrocatalytic hydrogen evolution
2025 · External reference
MOF-based electrocatalysts: an overview from the perspective of structural design
10.1021/acs.chemrev.4c00664 · 2025 · External reference
Material innovations and system challenges in hydrogen storage: a comprehensive review
2026 · External reference
Artificial intelligence in accelerating materials discovery: opportunities and challenges
2026 · External reference
Artificial intelligence-driven materials design for next-generation sustainable energy technologies
10.1021/acssuschemeng.6c01084 · 2026 · External reference
Artificial intelligence-driven approaches for materials design and discovery
10.1038/s41563-025-02403-7 · 2026 · External reference
Machine learning for accelerating energy materials discovery: bridging quantum accuracy with computational efficiency
2026 · External reference
Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligence
10.1007/s10845-025-02568-7 · 2026 · External reference
Artificial intelligence shaping the future of hydrogen technologies
2026 · External reference
Artificial intelligence-driven innovations in hydrogen storage technology
10.1002/eem2.70041 · 2025 · External reference
Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning
10.1016/j.apenergy.2025.125346 · 2025 · External reference
Predicting hydrogen storage in MOFs via machine learning
10.1016/j.patter.2021.100291 · 2021 · External reference
AI-driven optimization of hydrogen storage in porous carbon adsorbents
10.1038/s41598-026-45915-1 · 2026 · External reference
Exploring quantum support vector regression for predicting hydrogen storage capacity of nanoporous materials
10.1002/aidi.202500015 · 2025 · External reference
Advancing hydrogen storage predictions in metal-organic frameworks: a comparative study of LightGBM and random forest models with data enhancement
10.1016/j.ijhydene.2024.04.230 · 2024 · External reference
FusionStackBoost: a machine learning approach for accurate prediction of hydrogen storage performance in metal organic frameworks
10.1016/j.ijhydene.2025.04.506 · 2025 · External reference
Prediction of hydrogen storage in metal-organic frameworks using CatBoost-based approach
10.1016/j.ijhydene.2024.07.078 · 2024 · External reference
Advancements in metal organic frameworks for energy storage applications: a review of current research and future directions
10.53022/oarjet.2025.9.2.0088 · 2025 · External reference
Hydrogen energy storage with artificial intelligent-powered strategies for a sustainable future: a review
10.1007/s12206-025-0240-3 · 2025 · External reference
Hydrogen energy storage via carbon-based materials: from traditional sorbents to emerging architecture engineering and AI-driven optimization
10.3390/en18153958 · 2025 · External reference
AI-driven advances in metal–organic frameworks: from data to design and applications
10.1039/d5cc04220h · 2025 · External reference
Simple equations rival ML in predicting hydrogen storage in MOFs
10.1557/s43577-026-01130-x · 2026 · External reference
Advancing hydrogen storage: explainable machine learning models for predicting hydrogen uptake in metal–organic frameworks
10.1016/j.rineng.2025.107304 · 2025 · External reference
Machine learning-derived stage-specific design rules for metal–organic framework selection in seasonal hydrogen storage
10.1038/s41598-026-35073-9 · 2026 · External reference
Multitask transfer learning framework for predicting hydrogen storage performance of metal/covalent-organic frameworks across diverse operating conditions
10.1016/j.seppur.2025.134533 · 2025 · External reference
Machine learning-optimized bimetallic MOF/MXene composite with improved supercapacitor performance
10.1039/d5nj03839a · 2026 · External reference
Simulation of hydrogen storage in MOF-5 and Li-MOF-5 by ring polymer molecular dynamics on self-consistently fine-tuned machine-learned interatomic potentials
10.1021/acs.jpcc.5c01522 · 2025 · External reference
Metal–organic framework heterojunctions for photocatalysis
10.1039/d3cs00205e · 2024 · External reference
Metal–organic frameworks as photocatalysts for solar-driven overall water splitting
10.1021/acs.chemrev.2c00460 · 2023 · External reference
Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions
10.1039/d4cs00432a · 2025 · External reference
Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications
10.1039/d4ta06740a · 2025 · External reference
On the shoulders of high-throughput computational screening and machine learning: design and discovery of MOFs for H2 storage and purification
2023 · External reference
Computational modeling guided design of metal–organic frameworks for photocatalysis – a mini review
10.1039/d3cy00862b · 2023 · External reference
From data to discovery: recent trends of machine learning in metal–organic frameworks
10.1021/jacsau.4c00618 · 2024 · External reference
Artificial intelligence and high-throughput computational workflows empowering the fast screening of metal–organic frameworks for hydrogen storage
10.1021/acsami.4c06416 · 2024 · External reference
Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions
10.1016/j.ijhydene.2024.03.223 · 2024 · External reference
Reimagining metal-organic framework discovery: integrating experiment, computation, and artificial intelligence
10.1016/j.chempr.2025.102921 · 2026 · External reference
Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications
10.1038/s41598-026-44340-8 · 2026 · External reference
Machine learning and AI empowering metal–organic frameworks: synthesis, performance prediction and therapeutic application
10.1039/d5dt02184g · 2026 · External reference
3D metal-organic framework-based photocatalysts for environmental remediation: structural features, mechanistic insights, and emerging strategies
10.1002/tcr.202500296 · 2026 · External reference
Metal-organic-framework-based materials as platforms for energy applications
10.1016/j.chempr.2023.09.009 · 2024 · External reference
From metal-organic frameworks (MOFs) to metal-doped MOFs (MDMOFs): current and future scenarios in environmental catalysis and remediation applications
10.1016/j.microc.2023.108954 · 2023 · External reference
Precise direct patterning of metal-organic frameworks: chemical mechanisms, strategies and applications
2026 · External reference
Reprogramming porosity: the synthetic evolution of pore engineering in metal–organic frameworks
10.1021/acsmaterialslett.6c00074 · 2026 · External reference
Deciphering self-assembly mechanisms of IRMOF-n-inspired three-dimensional cubic-symmetry nanoporous crystals from multiscale simulations
10.1021/acs.chemmater.5c00527 · 2025 · External reference
Modified UiO-66 and its applications in environmental and energy fields
10.1002/cjoc.202401244 · 2025 · External reference
Post-synthetic modification of MOF-808: innovative strategies, structural and performance regulation
10.1039/d5qi01816a · 2026 · External reference
ZIF-8 metal–organic frameworks and their hybrid materials: emerging photocatalysts for energy and environmental applications
10.1039/d4dt02662d · 2025 · External reference
High-throughput DFT screening of single-metal and high-entropy MOF-74 for selective CO2/N2 separation and H2 storage
10.1016/j.ijhydene.2025.151276 · 2025 · External reference
Metal–organic frameworks for hydrogen and methane storage
2026 · External reference
Low-dimensional MOF nanoarchitectonics: progress in MOF-2D material hybrid architectures for energy conversion and storage
10.1002/adma.202521053 · 2026 · External reference
Emerging pristine MOF-based heterostructured nanoarchitectures: advances in structure evolution, controlled synthesis, and future perspectives
10.1002/smll.202303884 · 2023 · External reference
Recent progress of advanced conductive metal–organic frameworks: precise synthesis, electrochemical energy storage applications, and future challenges
2022 · External reference
Modifying MOF electrocatalysts for enhanced oxygen and hydrogen evolution reactions
2026 · External reference
One-step modulated hydrothermal synthesis of nickel-doped HKUST-1 for improved hydrogen storage performance
10.1002/sstr.202500839 · 2026 · External reference
Applications of metal–organic framework-derived N, P, S doped materials in electrochemical energy conversion and storage
10.1016/j.ccr.2022.214602 · 2022 · External reference
Enhanced green hydrogen generation via photocatalytic water splitting using V-doped Ti-squarate MOFs
10.1021/jacs.5c17654 · 2026 · External reference
Light-promoted hydrazine dehydrogenation over Ni/NH2-MIL-125: unraveling mechanisms for efficient hydrogen production
10.1002/advs.202519320 · 2026 · External reference
Enhanced hydrogen storage in metal–organic framework/graphene oxide composites: experimental characterization and molecular simulations
10.1021/acs.langmuir.6c01098 · 2026 · External reference
Interface engineering of HOF/MOF composites: synergistic design of S-scheme heterojunction and active sites for high-efficiency photocatalytic hydrogen production
2026 · External reference
Exceptional long-term stability in hydrogen evolution via defect-engineered MIL-100 synthesized by controlled thermolysis
10.1021/acs.inorgchem.5c04976 · 2026 · External reference
Synthesis of carboxyl-functionalized g-C3N4 supported Cu-MOFs photocatalyst and its efficient photocatalytic hydrogen production
10.1016/j.ijhydene.2025.151243 · 2025 · External reference
Recent breakthroughs in sulfide-functionalized metal–organic frameworks for electrocatalytic carbon dioxide reduction and hydrogen evolution reactions and their life cycle assessment
10.1016/j.ccr.2025.217029 · 2026 · External reference
Tailoring MIL-101 for enhanced hydrogen storage via functional group introduction and Li+ doping
10.1016/j.jssc.2025.125747 · 2026 · External reference
Advanced metal–organic frameworks for selective hydrogen separation and purification
10.1007/978-981-95-8142-9_5 · 2026 · External reference
Pristine metal–organic frameworks and their composites for renewable hydrogen energy applications
10.1002/adfm.202203224 · 2023 · External reference
Enhanced supercapacitor and catalytic properties of CuMn-MOF/Ag composites for energy storage and hydrogen evolution
10.1016/j.jpcs.2025.112632 · 2025 · External reference
MXene-MOF hybrid nanocomposite in hydrogen production: challenges, strategies, and future perspectives
10.1016/j.molstruc.2025.143800 · 2026 · External reference
Quasi-type-II Cu-In-Zn-S/Ni-MOF heterostructure with prolonged carrier lifetime for photocatalytic hydrogen production
10.1016/j.jcis.2024.02.095 · 2024 · External reference
Advanced MOF/MXene heterostructure with carbon aerogel to boost the ions movement in asymmetric supercapacitors and hydrogen production
10.1016/j.jpowsour.2024.235486 · 2024 · External reference
2D-MOF/2D-MOF heterojunctions with strong hetero-interface interaction for enhanced photocatalytic hydrogen evolution
10.1007/s12598-023-02387-w · 2023 · External reference
Recent research progress of MOFs-based heterostructures for electrocatalytic hydrogen evolution
10.1016/j.ijhydene.2026.154438 · 2026 · External reference
Charge-induced defective MOF-801 with enhanced sorption heat for efficient hydrogen storage
2025 · External reference
Defects engineering of metal–organic framework immobilized Ni-La(OH)3 nanoparticles for enhanced hydrogen production
10.1016/j.apcatb.2022.121989 · 2023 · External reference
Optimizing the piezocatalytic hydrogen production activity of metal–organic frameworks through precise defect engineering
10.1039/d5ta10240e · 2026 · External reference
Switching on the photocatalysis of metal–organic frameworks by engineering structural defects
10.1002/anie.201907074 · 2019 · External reference
Diatomic catalytic sites on MOF-derived architecture towards effective H2 production
10.1016/j.cej.2025.169447 · 2025 · External reference
Research progress of defect-engineered UiO-66(Zr) MOFs for photocatalytic hydrogen production
10.1007/s11708-021-0765-9 · 2021 · External reference
Activating a metallization switch for record hydrogen evolution in single-atom modified polar MOF piezocatalysts
2026 · External reference
Enhancing electrochemical hydrogen storage in nickel-based metal–organic frameworks (MOFs) through zinc and cobalt doping as bimetallic MOFs
10.1016/j.ijhydene.2024.12.456 · 2025 · External reference
Utilizing crystal defects to boost metal–organic frameworks hydrogen generation abilities
10.1016/j.micromeso.2019.109943 · 2020 · External reference
Enhancing hydrogen storage in UiO-series metal–organic frameworks via ligand functionalization and metal substitution engineering
10.1021/acs.energyfuels.5c04793 · 2025 · External reference
Atomically precise MOF-based electrocatalysts by design: hydrogen evolution applications
2025 · External reference
Single-atom based metal–organic framework photocatalysts for solar-fuel generation
10.1002/smll.202401389 · 2024 · External reference
Recent studies on the construction of MOF-based composites and their applications in photocatalytic hydrogen evolution
10.3390/molecules30132755 · 2025 · External reference
Metal–organic frameworks for high-efficiency solid-state hydrogen storage: design, synthesis, regulation, and prospects
10.1063/5.0310503 · 2026 · External reference
Structure, function and synthesis techniques of two-dimensional metal–organic frameworks (2D MOFs) for energy storage and conversion application
2026 · External reference
Rational crystal engineering of metal–organic frameworks for tailored structure and function
10.1039/d5ce00974j · 2026 · External reference
Recent advances in transition metal-based metal–organic frameworks for hydrogen production
10.1002/smsc.202400446 · 2025 · External reference
Designing hydrostable MOFs for photoelectrochemical water splitting: a review on progress, stability challenges, and future directions
10.1016/j.jece.2026.123161 · 2026 · External reference
Pristine metal–organic framework electrocatalysts for hydrogen production: role of electrocatalyst properties in basic media
2025 · External reference
A review on MOFs synthesis and effect of their structural characteristics for hydrogen adsorption
10.1039/d4ra00865k · 2024 · External reference
Recent developments in the field of MOF-based catalytic systems: a review
10.1016/j.apcata.2025.120555 · 2025 · External reference
Unveiling cutting-edge progress in coordination chemistry of the metal–organic frameworks (MOFs) and their composites: fundamentals, synthesis strategies, electrochemical and environmental applications
10.1016/j.jiec.2025.01.033 · 2025 · External reference
Review: synthesis of metal–organic frameworks and applications in gas adsorption, separation and storage
10.1007/s10853-026-13053-8 · 2026 · External reference
Chapter 4 - microwave-assisted synthesis of metal–organic frameworks
2024 · External reference
The properties of microwave-assisted synthesis of metal–organic frameworks and their applications
10.3390/nano13020352 · 2023 · External reference
Preparation and hydrogen storage properties of metal–organic framework UiO-66: comparison of microwave and conventional hydrothermal preparation
10.1016/s1872-5813(24)60496-2 · 2025 · External reference
Standardized protocols and applications in mechanochemical synthesis for organic and inorganic materials
10.1007/s44371-026-00526-7 · 2026 · External reference
Investigation of cryo-adsorption hydrogen storage capacity of rapidly synthesized MOF-5 by mechanochemical method
10.1016/j.ijhydene.2022.11.026 · 2023 · External reference
Sustainable fabrication of metal-organic frameworks for improved hydrogen storage
10.1016/j.ijhydene.2024.07.248 · 2024 · External reference
Linking mechanochemistry with the green chemistry principles: review article
10.1016/j.heliyon.2024.e34655 · 2024 · External reference
Metal–organic frameworks as advanced photoelectrodes for photoelectrochemical water splitting: design, mechanisms, and challenges
10.1021/acs.energyfuels.6c00130 · 2026 · External reference
Bifunctional MOF-5/FeCo2O4 hybrid nanostructures for high-performance supercapacitors and hydrogen evolution reactions
10.1021/acsanm.6c00672 · 2026 · External reference
Review—Direct electrochemical synthesis of metal organic frameworks
2020 · External reference
Chapter 7 - sonochemical synthesis of metal–organic frameworks
2024 · External reference
Facile synthesis of iron-based MIL-101 metal-organic framework as a potential hydrogen storage material
10.1007/s10904-026-04249-1 · 2026 · External reference
High-performance catalytic systems for superior photo- and electrochemical hydrogen production
10.1039/d6cp00641h · 2026 · External reference
Photo(electro)catalytic water splitting for hydrogen production: mechanism, design, optimization, and economy
10.3390/molecules30030630 · 2025 · External reference
A review on electrochemical water splitting electrocatalysts for green H2 production: unveiling the fundamentals and recent advances
10.1021/acs.langmuir.5c00138 · 2025 · External reference
Multifunctional Z-scheme Bi-MOF/g-C3N4 photocatalyst for pharmaceutical degradation, hydrogen evolution, and electricity generation
10.1039/d6ta00414h · 2026 · External reference
Rational design of NiNCo3 and NiFe-MOF@NiNCo3 nanosheets as efficient monolithic electrocatalysts for water splitting
10.1016/j.ijhydene.2026.154559 · 2026 · External reference
A review on hydrogen production using decorated metal-organic frameworks by electrocatalytic and photocatalytic water splitting
10.1016/j.fuel.2025.134416 · 2025 · External reference
Cu-based MOF/TiO2 composite nanomaterials for photocatalytic hydrogen generation and the role of copper
10.1002/adfm.202501736 · 2026 · External reference
Synthesis of MOF/MoS2 composite photocatalysts with enhanced photocatalytic performance for hydrogen evolution from water splitting
10.1016/j.ijhydene.2021.10.021 · 2022 · External reference
Metal–organic framework/graphene-based nanocatalysts for hydrogen evolution reaction: a comprehensive review
10.1016/j.ijhydene.2024.12.132 · 2025 · External reference
Silver nanoparticles-decorated graphene oxide/MOF composite for efficient photocatalytic and photoelectrocatalytic hydrogen evolution
10.1016/j.ijoes.2026.101370 · 2026 · External reference
A multifunctional nickel-based metal–organic framework (MOF) for hydrogen production, supercapacitors, and electrocatalysis
10.3390/catal16030283 · 2026 · External reference
Interface-engineered CoNi-MOFs with accelerated charge-transfer kinetics for enhanced OER catalysis and superior faradaic H2/O2 generation in solar-driven water splitting
2026 · External reference
Interface-engineered ZnS QDs@HKUST-1 composite for electrochemical overall water splitting
10.1039/d6nr01383j · 2026 · External reference
Design, photocatalytic and electrochemical performance of a Ni-MOF@CoO composite as a durable catalyst for hydrogen evolution and water remediation
2026 · External reference
Enhanced charge regulation in a bimetallic CoFe-MOF/g-C3N4 hybrid for efficient hydrogen evolution
10.1007/s10853-026-12683-2 · 2026 · External reference
Chapter 9 - machine learning fundamentals for hydrogen production and storage
2026 · External reference
A combination of multi-scale calculations with machine learning for investigating hydrogen storage in metal organic frameworks
10.1016/j.ijhydene.2021.06.021 · 2021 · External reference
Toward intelligent design of solid-state hydrogen storage: trends, challenges, and machine learning insights
10.1007/s11705-026-2649-3 · 2026 · External reference
Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks
10.1016/j.ces.2022.117813 · 2022 · External reference
Machine-learning-guided design of MOF-based electrocatalysts for sustainable ammonia production
10.1039/d5cc07118f · 2026 · External reference
Machine learning-driven nanoscale synthesis for electrocatalytic performance: from data-driven methodologies to closed-loop optimization
2025 · External reference
Artificial intelligence-based prediction of hydrogen uptake of metal organic frameworks with high volumetric storage density for fuel cell systems
2025 · External reference
The transformative role of machine learning in advancing MOF membranes for gas separations
10.1063/5.0278371 · 2025 · External reference
Application of machine learning in MOFs for gas adsorption and separation
10.1088/2053-1591/ad0c07 · 2023 · External reference
Targeted metal–organic framework discovery goes digital: machine learning's quest from algorithms to atom arrangements
10.1007/s42114-024-01044-9 · 2024 · External reference
Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks
10.1039/d0me00005a · 2020 · External reference
From computational high-throughput screenings to the lab: taking metal–organic frameworks out of the computer
10.1039/d2sc01254e · 2022 · External reference
Descriptors construction and application in catalytic site design
10.1016/j.isci.2025.113080 · 2025 · External reference
Machine learning-driven high-throughput screening of electrocatalysts and electrolytes for electrochemical surfaces and interfaces
10.1039/d6cc01112h · 2026 · External reference
Bridging machine learning and water electrolysis: concepts, methods, and perspectives
2026 · External reference
Regression
2022 · External reference
Recent developments in the use of machine learning in catalysis: a broad perspective with applications in kinetics
10.1016/j.cej.2025.160872 · 2025 · External reference
A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction
10.1038/s41524-025-01607-4 · 2025 · External reference
Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
10.1039/d4cs00844h · 2024 · External reference
Data analysis and machine learning aided integrated catalyst activity and process modelling for selective H2 production from biomass gasification
10.1016/j.biombioe.2024.107291 · 2024 · External reference
Chapter 9 - artificial neural network and its applications: unraveling the efficiency for hydrogen production
2021 · External reference
Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production
10.1038/s41598-024-75068-y · 2024 · External reference
Analysis and prediction of hydrogen yield using artificial neural networks
2026 · External reference
Experimental evaluation of mathematical and artificial neural network modeling of energy storage system
2018 · External reference
High-performance hydrogen energy generation via innovative metal-organic framework catalysts and integrated system design
10.1038/s41598-025-08306-6 · 2025 · External reference
Machine learning for gas adsorption in metal–organic frameworks: a review on predictive descriptors
10.1021/acs.iecr.4c03500 · 2025 · External reference
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
10.1016/j.matt.2021.02.015 · 2021 · External reference
Machine learning for catalytic reaction systems: a framework for complex chemical processes
10.1021/acsengineeringau.5c00093 · 2026 · External reference
Machine learning aided synthesis and screening of HER catalyst: present developments and prospects
10.1080/01614940.2022.2103980 · 2024 · External reference
Computational intelligence approaches for hydrogen storage material design
10.1002/cjce.70232 · 2026 · External reference
Machine learning toward advanced energy storage devices and systems
10.1016/j.isci.2020.101936 · 2021 · External reference
Advancing polymer science and energy storage solutions through the integration of artificial intelligence and machine learning: a transformative approach
10.3390/polym17243267 · 2025 · External reference
Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials
10.1039/d3ya00104k · 2023 · External reference
Applications of random forest in multivariable response surface for short-term load forecasting
10.1016/j.ijepes.2022.108073 · 2022 · External reference
Prediction of hydrogen uptake of metal organic frameworks using explainable machine learning
10.1016/j.egyai.2023.100230 · 2023 · External reference
Hydrogen storage capacity in metal-organic frameworks: towards elevating predictions through ensemble learning with a comprehensive preprocessed dataset
10.1016/j.ijhydene.2025.03.042 · 2025 · External reference
Machine learning aided investigation on the structure-performance correlation of MOF for membrane-based He/H2 separation
10.1016/j.gce.2024.01.005 · 2024 · External reference
Interpretable machine learning-guided plasma catalysis for hydrogen production
10.1038/s44286-025-00287-7 · 2025 · External reference
Smart reforming for hydrogen production via machine learning
10.1016/j.ces.2024.120959 · 2025 · External reference
Chapter 9 - support vector machine, an important supervised learning category
2026 · External reference
Support vector machines and support vector regression
2022 · External reference
Comprehensive review on twin support vector machines
10.1007/s10479-022-04575-w · 2024 · External reference
Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization
10.1016/j.aca.2004.12.024 · 2005 · External reference
Machine learning and descriptor selection for the computational discovery of metal-organic frameworks
10.1080/08927022.2021.1916014 · 2021 · External reference
Application of soft computing represented by regression machine learning model and artificial lemming algorithm in predictions for hydrogen storage in metal-organic frameworks
10.3390/ma18133122 · 2025 · External reference
Enhanced hydrogen storage efficiency with sorbents and machine learning: a review
10.1007/s10311-024-01741-3 · 2024 · External reference
Machine learning meets with metal organic frameworks for gas storage and separation
10.1021/acs.jcim.1c00191 · 2021 · External reference
Ensemble learning enables accurate and interpretable identification of hydrogen storage in 2D materials beyond MXenes
2026 · External reference
Investigation of wettability and IFT alteration during hydrogen storage using machine learning
10.1016/j.heliyon.2024.e38679 · 2024 · External reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks
10.1039/c8me00050f · 2019 · External reference
Gradient boosted machine learning model to predict H2, CH4, and CO2 uptake in metal–organic frameworks using experimental data
10.1021/acs.jcim.3c00135 · 2023 · External reference
Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage
10.1080/08927022.2019.1597271 · 2019 · External reference
Exploring the usefulness of Gaussian process regression for the prediction of oil, water and gas production rates
2023 · External reference
Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach
10.1038/s41598-022-26522-2 · 2022 · External reference
Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites
10.1080/00268976.2019.1658910 · 2019 · External reference
Gaussian process regression for prediction of hydrogen adsorption temperature–pressure dependence curves in metal–organic frameworks
10.1016/j.cej.2023.146553 · 2023 · External reference
Coupling data-driven and reinforcement learning for material development and device management in batteries
2026 · External reference
Intelligent screening of porous materials: a review of active-learning approaches in MOF research
2025 · External reference
Artificial intelligence paradigms for next-generation metal–organic framework research
10.1021/jacs.5c08214 · 2025 · External reference
Machine learning to design metal–organic frameworks: progress and challenges from a data efficiency perspective
10.1039/d5mh01467k · 2026 · External reference
Data-driven explainable machine learning approaches for predicting hydrogen adsorption in porous crystalline materials
10.1016/j.jallcom.2025.180709 · 2025 · External reference
Chemical-guided screening of top-performing metal–organic frameworks for hydrogen storage: an explainable deep attention convolutional model
10.1016/j.cej.2024.155626 · 2024 · External reference
Graph neural network-based multi-objective Bayesian optimization for enhanced screening of metal–organic frameworks with optimal separation performance
10.1039/d5ta09133k · 2026 · External reference
Seeking metal–organic frameworks for hydrogen storage using classical and quantum active learning
10.1039/d5cp02747k · 2025 · External reference
Physics-informed machine learning for fast screening high hydrogen storage MOFs with monotonicity constraints
10.1021/acs.jctc.5c00242 · 2025 · External reference
Physics-informed analytical models for interpretable and deployable hydrogen storage prediction in MOFs
10.1103/glnw-drn4 · 2025 · External reference
Accelerating the discovery and optimization of metal-organic framework materials via machine learning
10.1016/j.cis.2025.103671 · 2025 · External reference
Crystal structure prediction and performance assessment of hydrogen storage materials: insights from computational materials science
10.3390/en17143591 · 2024 · External reference
Recent advances in metal–organic frameworks for solid-state hydrogen storage: synthesis, optimization, and perspectives
10.1016/j.ijhydene.2025.151746 · 2025 · External reference
Machine learning-assisted design of metal–organic frameworks for hydrogen storage: a high-throughput screening and experimental approach
10.1016/j.cej.2025.160766 · 2025 · External reference
Computational investigation of the impact of metal–organic framework topology on hydrogen storage capacity
10.1039/d5me00078e · 2025 · External reference
Prediction of hydrogen storage capacities in metal–organic frameworks by using machine learning methods with a small dataset
10.1016/j.cplett.2025.142179 · 2025 · External reference
Accelerating discovery through integration: a DFT validated machine learning framework for screening MOF photocatalysts
10.1039/d5ta08107f · 2026 · External reference
Machine learning guided discovery of water stable metal–organic frameworks for photocatalytic hydrogen production
10.1039/d5sc08277c · 2026 · External reference
Unraveling the recent advancement of single-atom catalysts derived from metal–organic frameworks for sustainable electrocatalysis
10.1021/acssuschemeng.5c01382 · 2025 · External reference
Effective screening descriptors of metal–organic framework-supported single-atom catalysts for electrochemical CO2 reduction reactions: a computational study
10.1021/acscatal.4c03937 · 2024 · External reference
High-throughput screening of Ru-based MOF-Supported single-atom catalysts for hydrogen evolution reaction via machine learning interatomic potential
10.1021/acscatal.5c06272 · 2025 · External reference
Machine-learning-assisted high-throughput computational screening of metal–organic framework membranes for hydrogen separation
10.1016/j.cej.2022.136783 · 2022 · External reference
Machine-learning-assisted high-throughput computational study of CH4/H2 adsorption and separation in anion-pillared MOFs
10.1016/j.ijhydene.2025.03.115 · 2025 · External reference
Unlocking the potential of ionic liquids in anion-pillared MOFs for enhanced He/H2 separation performance: a combined computational screening and machine learning study
10.1016/j.seppur.2025.132253 · 2025 · External reference
Machine learning confirms the formation mechanism of a single-atom catalyst via infrared spectroscopic analysis
10.1021/acs.jpclett.3c02896 · 2023 · External reference
Two-dimensional interaction parameter histograms as a simple and versatile nanoporous material representation for machine learning prediction of adsorption properties
10.1039/d6me00034g · 2026 · External reference
Topological data analysis enhanced prediction of hydrogen storage in metal–organic frameworks (MOFs)
10.1039/d3ma00591g · 2024 · External reference
Identifying MOFs for electrochemical energy storage via density functional theory and machine learning
10.1038/s41524-025-01590-w · 2025 · External reference
Predicting hydrogen storage in metal-organic frameworks using a novel hybrid machine learning model
10.1016/j.ijhydene.2025.06.112 · 2025 · External reference
Innovative strategies to significantly boost photocatalytic hydrogen production: from high-performance photocatalysts to potential industrialization
10.20517/energymater.2025.128 · 2026 · External reference
Predicting hydrogen storage in MOFs: representation matters
10.1016/j.ijhydene.2026.156056 · 2026 · External reference
A new machine learning framework for efficient MOF discovery: application to hydrogen storage
10.1016/b978-0-323-85159-6.50301-8 · 2022 · External reference
Recent advances in renewable hydrogen purification technologies: a general review
10.3390/cleantechnol8020035 · 2026 · External reference
Integrating hydrogen production and subsurface storage: toward a sustainable hydrogen economy: a critical review
10.1007/s44421-026-00017-6 · 2026 · External reference
Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes
10.1016/j.seppur.2023.126001 · 2024 · External reference
Exploring hydrogen storage capacity in metal–organic frameworks: a Bayesian optimization approach
10.1002/chem.202301840 · 2023 · External reference
A multi-objective optimization-driven screening approach for maximizing hydrogen storage capacities in MOFs
10.1038/s41598-025-09654-z · 2026 · External reference
Use of machine learning models as surrogate models for finding regions of structural properties of MOFs with high hydrogen storage capacities at room temperature and moderate pressures
10.1016/j.chemphys.2026.113209 · 2026 · External reference
Smart screening of hydrogen storage capacities in MOFs using a tailored machine learning
10.1016/j.nxener.2025.100431 · 2025 · External reference
Graph-based machine learning framework for predicting hydrogen storage capacity in metal–organic frameworks
10.1021/acs.jcim.5c01528 · 2025 · External reference
Machine-learning-based prediction of hydrogen adsorption capacity at varied temperatures and pressures for MOFs adsorbents
10.1016/j.jtice.2022.104479 · 2022 · External reference
Accelerating the practical application of MOFs for hydrogen Storage—From performance-driven to application-oriented
10.1016/j.gee.2024.03.007 · 2024 · External reference
High-throughput screening of experimental metal–organic frameworks for high water content syngas hydrogen purification under pressure swing adsorption conditions
10.1021/acsami.5c19342 · 2026 · External reference
Domain-trained language model for inverse design and synthesis of high-performance hydrogen storage MOFs
2026 · External reference
Interpretable machine learning unveils hydroxyl/amino synergy and guides discovery of optimal MOF photocatalysts for hydrogen evolution
10.1021/jacs.6c05998 · 2026 · External reference
Predicting hydrogen deliverability in real metal-organic frameworks using machine learning
10.1016/j.ijhydene.2026.155970 · 2026 · External reference
Machine learning in the design and performance prediction of organic framework membranes: methodologies, applications, and industrial prospects
10.3390/membranes15060178 · 2025 · External reference
Explainable machine learning reveals key drivers of hydrogen-rich syngas in steam co-gasification of multi-source organic wastes
10.1016/j.ijhydene.2025.152525 · 2026 · External reference
Next-generation composite materials and manufacturing: a review of smart, sustainable, and digital advancements
10.1002/admt.202501409 · 2026 · External reference
Balancing nucleation and growth kinetics enables fully-coordinated acetic acid-tethered metal–organic frameworks for technoeconomic-viable hydrogen storage
10.1002/aenm.202503259 · 2025 · External reference
Post-hybridization of MIL-101(Cr) with graphene oxide enhances its hydrogen storage and release capacities
2025 · External reference
Metal–organic framework (MOF) as adsorbents for hydrogen separation from steam methane reforming: an in-depth review
10.1007/s11356-025-36963-7 · 2025 · External reference
Process-informed MOF–zeolite selection for gasification-derived hydrogen purification: explainable TSA simulations reveal regime-specific adsorbent advantages
2026 · External reference
Photo-electrocatalytic hydrogen production emphasising process scalability
10.3390/en19174177 · 2026 · External reference
Comparative assessment and review of hydrogen storage technologies: materials, System architectures, performance metrics, challenges, economics analysis and safety considerations
2026 · External reference
Artificial intelligence meets laboratory automation in discovery and synthesis of metal–organic frameworks: a review
10.1021/acs.iecr.4c04636 · 2025 · External reference
Identification of stable intermetallic compounds for hydrogen storage via machine learning
10.1002/est2.70115 · 2025 · External reference
Large language models (LLMs) for materials design
2026 · External reference
Pristine metal–organic frameworks and their composites for renewable hydrogen energy applications
10.1002/adfm.202203224 · ExternalCitation · doi-reference
Cu-based MOF/TiO2 composite nanomaterials for photocatalytic hydrogen generation and the role of copper
10.1002/adfm.202501736 · ExternalCitation · doi-reference
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10.1002/adma.202504364 · ExternalCitation · doi-reference
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10.1002/adma.202521053 · ExternalCitation · doi-reference
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10.1002/admt.202501409 · ExternalCitation · doi-reference
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10.1002/advs.202519320 · ExternalCitation · doi-reference
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10.1002/aenm.202503259 · ExternalCitation · doi-reference
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10.1002/aidi.202500015 · ExternalCitation · doi-reference
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10.1002/anie.201907074 · ExternalCitation · doi-reference
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10.1002/chem.202301840 · ExternalCitation · doi-reference
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10.1002/cjce.70232 · ExternalCitation · doi-reference
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10.1002/cjoc.202401244 · ExternalCitation · doi-reference
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10.1002/cphc.202500459 · ExternalCitation · doi-reference
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10.1002/eem2.70041 · ExternalCitation · doi-reference
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10.1002/ep.70229 · ExternalCitation · doi-reference
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10.1002/ep.70328 · ExternalCitation · doi-reference
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10.1002/smsc.202400446 · ExternalCitation · doi-reference
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Prediction of hydrogen adsorption in nanoporous materials from the energy distribution of adsorption sites
10.1080/00268976.2019.1658910 · ExternalCitation · doi-reference
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Molecular modelling and machine learning for high-throughput screening of metal-organic frameworks for hydrogen storage
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