Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement-Reference-Cited by-同舟云学术

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Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

Published:2024-06 Issue: Volume:69 Page:158-172
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Seyyedattar Masoud^ORCID,Zendehboudi Sohrab^ORCID,Ghamartale Ali,Afshar Majid^ORCID

Publisher

Elsevier BV

Reference72 articles.

1. Direct air capture: process technology, techno-economic and socio-political challenges;Erans;Energy Environ Sci,2022

2. Direct air capture of CO2 via aqueous-phase absorption and crystalline-phase release using concentrated solar power;Brethomé;Nat Energy,2018

3. A review on current trends in potential use of metal-organic framework for hydrogen storage;Shet;Int J Hydrogen Energy,2021

4. Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks;Bucior;Mol Syst Des Eng,2019

5. Density functional theory study of hydrogen adsorption in a Ti-decorated Mg-based metal–organic framework-74;Suksaengrat;ChemPhysChem,2015

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1. Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality;Remote Sensing Applications: Society and Environment;2024-11

2. Hydrogen Storage Potential of XNiH3 (X= Sr and Ba) Compounds: A Comprehensive DFT Analysis;Physics Letters A;2024-09

3. Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive MOFs for ethane/ethylene separation;The Canadian Journal of Chemical Engineering;2024-08-20

4. Advanced EOR screening methodology based on LightGBM and random forest: A classification problem with imbalanced data;The Canadian Journal of Chemical Engineering;2024-08-08

5. Assessment of hydrogen production methods for global energy transition using AI enhanced quantum recommender fuzzy modelling;International Journal of Hydrogen Energy;2024-06

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