Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science

Xi Yang, Yuting Li, Yitao Liu, Qian Li, Tingna Yang () and Hongxing Jia ()
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Xi Yang: Yunnan Energy Research Institute Co., Ltd., Kunming 650299, China
Yuting Li: College of Materials Science and Engineering, National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing 400044, China
Yitao Liu: Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Qian Li: College of Chemistry and Molecular Science, Wuhan University, Wuhan 430072, China
Tingna Yang: Yunnan Energy Research Institute Co., Ltd., Kunming 650299, China
Hongxing Jia: College of Materials Science and Engineering, National Engineering Research Center for Magnesium Alloys, Chongqing University, Chongqing 400044, China

Energies, 2024, vol. 17, issue 14, 1-20

Abstract: Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials.

Keywords: hydrogen storage materials; density functional theory; molecular dynamics simulations; machine learning; crystal structure prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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