Evidence map›Paper›PMID 41484161›Full record

ArticleScientific reports2026

A multi-objective optimization-driven screening approach for maximizing hydrogen storage capacities in MOFs.

P Anbumani, Rohit Duvvuri, Sudha Radhika, Asif Abdul Azeez, Ravindran Sujith

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

P AnbumaniDepartment of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, 500078, India.
Rohit DuvvuriDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, 500078, India.
Sudha RadhikaDepartment of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, 500078, India.
Asif Abdul AzeezDepartment of Chemistry, University College, Thiruvananthapuram, 695034, India.
Ravindran SujithDepartment of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, 500078, India. sujith@hyderabad.bits-pilani.ac.in.

Funding

Science and Engineering Research Board CRG/2020/001066
6 · The paper itself

Abstract

The inherent tradeoff between gravimetric and volumetric hydrogen storage capacities in metal-organic frameworks (MOFs) limits their commercial viability. While benchmarked MOFs like MOF-5, IRMOF-20, and PCN-610 perform well at 77 K, maintaining their performance at elevated temperatures (298 K) remains challenging. To address this, a multi-objective particle swarm optimization framework was developed to identify promising MOFs for hydrogen storage. The optimization was guided by predictions from the bootstrapped-random forest trees. This optimization yielded 152 theoretical MOF feature combinations, which were matched with 733,792 existing structures. A nearest neighbor search identified 43 promising MOFs, with Zn-based MOF-2087 emerging as the global best, exhibiting consistent hydrogen storage performance across temperatures. Grand Canonical Monte Carlo simulations confirmed its high hydrogen uptake (5.3 wt% and 7.4 gH

Indexed as

Grand canonical Monte Carlo studiesHydrogen storageMetal-organic frameworksMolecular dynamicsMulti-objective optimizationSimulation

Identifiers

PMID41484161
PMCPMC12769568

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.