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Hydrides for Energy Storage: Materials, Technologies and Applications
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Hydrides for Energy Storage: Materials, Technologies and Applications

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Energy Materials".

Deadline for manuscript submissions: 10 August 2025 | Viewed by 233

Special Issue Editor

Dr. Chunguang Tang

E-Mail Website
Guest Editor
Institute for Frontier Materials, Deakin University, Geelong, VIC 3217, Australia
Interests: computational and experimental materials science of alloys; metal oxides; hydrogen storage materials

Special Issue Information

Dear Colleagues,

As the world pivots towards sustainable energy systems, the need for efficient, safe, and scalable energy storage has never been greater. Hydrides—encompassing metal hydrides, complex hydrides, and organic hydrides (i.e., liquid organic hydrogen carriers, LOHCs)—offer various solutions for hydrogen storage, batteries, fuel cells, and thermal energy storage. This Special Issue aims to highlight the latest breakthroughs across the spectrum of hydride-based materials and technologies, promoting research that spans fundamental science and practical applications.

We welcome submissions on a variety of topics, including, but not limited to, the following:

  • Development of Hydride Materials: Advances in metal, complex, and organic hydrides, including novel materials and hybrid systems.
  • Thermodynamics and Kinetics: Mechanistic studies of hydrogen absorption, desorption, diffusion, and chemical transformations.
  • Energy Storage Technologies: Hydrides for hydrogen storage, thermal energy storage, batteries, and fuel cells, including system integration challenges.
  • Processing and Manufacturing: Innovations in ball milling, additive manufacturing, and thin films for hydride-based systems.
  • Sustainability and Life Cycle Assessment: Environmental and economic evaluations of hydride technologies, including LOHCs.
  • Hydrogen Delivery and Refuelling: The application of hydrides and LOHCs for hydrogen logistics and refuelling infrastructures.

We look forward to receiving your submissions, contributing to shaping the future of energy storage with hydrides.

Prof. Dr. Chunguang Tang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metal hydrides
  • complex hydrides
  • liquid organic hydrogen carriers
  • hydrogen storage
  • thermal energy storage
  • fuel cells

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Published Papers (1 paper)

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0 pages, 5757 KiB  
Article
Application of Soft Computing Represented by Regression Machine Learning Model and Artificial Lemming Algorithm in Predictions for Hydrogen Storage in Metal-Organic Frameworks
by Jiamin Zhang, Yanzhe Li, Chuanqi Li, Xiancheng Mei and Jian Zhou
Materials 2025, 18(13), 3122; https://doi.org/10.3390/ma18133122 (registering DOI) - 1 Jul 2025
Abstract
Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random [...] Read more.
Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN). An improved population-based metaheuristic optimization algorithm, the artificial lemming algorithm (ALA), is employed to select the hyperparameters of these machine learning models, enhancing their performance. All developed models are trained and tested using experimental data from multiple studies. The performance of the models is evaluated using various statistical metrics, complemented by regression plots, error analysis, and Taylor graphs to further identify the most effective predictive model. The results show that the ALA-RF model obtains the best performance in predicting hydrogen storage, with optimal values of coefficient of determination (R2), root mean square error (RMSE), Willmott’s index (WI), and weighted average percentage error (WAPE) in both training and testing phases (0.9845 and 0.9840, 0.2719 and 0.2828, 0.9961 and 0.9959, and 0.0667 and 0.0714, respectively). Additionally, pressure is identified as the most significant feature for predicting hydrogen storage in MOFs. These findings provide an intelligent solution for the selection of MOFs and optimization of operational conditions in hydrogen storage processes. Full article
(This article belongs to the Special Issue Hydrides for Energy Storage: Materials, Technologies and Applications)
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