Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
May 6, 2024International Journal of Hydrogen EnergyOpen Access

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

View Full Paper
Ask AI
Bookmark
Share

Authors

MSMasoud SeyyedattarSZSohrab ZendehboudiAGAli Ghamartale

Discussion

Loading...

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Seyyedattar et al. (2024) studied this question.

synapsesocial.com/papers/68e6b4ceb6db643587635fa5https://doi.org/10.1016/j.ijhydene.2024.04.230
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Graph-Based Machine Learning Framework for Predicting Hydrogen Storage Capacity in Metal–Organic Frameworks2025 · 7 citations
  2. 2Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies2024 · 16 citations
  3. 3Accelerating discovery of MOFs for hydrogen storage via machine learning in energy related applications2026 · 3 citations
  4. 4Machine learning-driven optimization of solid-state hydrogen storage materials for sustainable energy applications2026
  5. 5Predicting hydrogen storage in MOFs: Representation matters2026