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

A. Granja-DelRío; F. Hernando-Galego; I. Cabria

doi:10.1016/j.chemphys.2026.113209

Publication record · 2026

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

Authors: A. Granja-DelRío · F. Hernando-Galego · I. Cabria
Year: 2026
Publisher: Elsevier
Journal or publication: Chemical Physics
Google Scholar citations: 2

Journal indicators

CiteScore 2025 (Scopus)4.3

Best quartileQ2

Top 10% in any categoryNo

Positions by category

Physics and Astronomy (all)79/248 · Q2 · percentile 68
Physical and Theoretical Chemistry104/194 · Q3 · percentile 46

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Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).

The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.

Research summary

Supervised machine-learning models act as efficient surrogates for costly molecular simulations, predicting usable hydrogen-storage capacity and identifying interpretable regions of the MOF design space at room temperature and moderate pressure.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.1016/j.chemphys.2026.113209 ↗ · Open-access version ↗ · View Google Scholar record ↗