人工智能驱动的储氢技术创新

IF 14.1 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yusong Ding, Lele Tong, Xiaolin Liu, Ying Liu, Yan Zhao
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引用次数: 0

摘要

在全球向可持续能源转型的过程中,氢能因其环境可持续性、零排放和高效率,已成为重塑能源格局不可或缺的支柱。然而,氢能的大规模部署在储存和运输方面面临着巨大的技术障碍。尽管当代研究已经将重点转移到高效储氢材料的开发上,但传统的材料设计概念仍然主要依靠经验,通常依赖于试错方法。重要的是,人工智能技术在加速材料发现和优化方面的广泛应用已经引起了相当大的关注。本文综述了储氢技术的最新进展,重点介绍了高通量筛选和机器学习在固态储氢材料中的协同应用。这些方法在准确预测储氢性能、优化材料性能和加速创新储氢材料的开发方面显示出非凡的潜力。具体来说,我们详细讨论了人工智能在开发储氢材料(如金属氢化物、合金、碳材料、金属有机框架和沸石)中的重要作用。此外,地下储氢作为一种可扩展的可再生能源存储解决方案,特别是在优化存储参数和性能预测方面进行了进一步探索。通过系统分析现有储氢方法的局限性和人工智能驱动方法的变革潜力,本综述为高性能储氢材料的发现和优化提供了见解,为全球可持续能源发展和技术创新做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial Intelligence-Driven Innovations in Hydrogen Storage Technology

Artificial Intelligence-Driven Innovations in Hydrogen Storage Technology

In the global transition towards sustainable energy sources, hydrogen energy has emerged as an indispensable pillar in reshaping the energy landscape, owing to its environmental sustainability, zero emissions, and high efficiency. Nevertheless, the large-scale deployment of hydrogen energy is confronted with substantial technical barriers in storage and transportation. Although contemporary research has shifted focus to the development of highly efficient hydrogen storage materials, conventional material design concepts remain predominantly empirical, typically relying on trial-and-error methodologies. Importantly, the widespread application of artificial intelligence technologies in accelerating materials discovery and optimization has attracted considerable attention. This review provides a comprehensive overview of the latest advancements in hydrogen storage technologies, with an emphasis on the synergistic application of high-throughput screening and machine learning in solid-state hydrogen storage materials. These approaches demonstrate exceptional potential in accurately predicting hydrogen storage properties, optimizing material performance, and accelerating the development of innovative hydrogen storage materials. Specifically, we discuss in detail the essential role of artificial intelligence in developing hydrogen storage materials such as metal hydrides, alloys, carbon materials, metal–organic frameworks, and zeolites. Moreover, underground hydrogen storage is further explored as a scalable renewable energy storage solution, particularly in terms of optimizing storage parameters and performance prediction. By systematically analyzing the limitations of existing hydrogen storage approaches and the transformative potential of artificial intelligence-driven methods, this review offers insights into the discovery and optimization of high-performance hydrogen storage materials, contributing to sustainable global energy development and technological innovation.

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来源期刊
Energy & Environmental Materials
Energy & Environmental Materials MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
17.60
自引率
6.00%
发文量
66
期刊介绍: Energy & Environmental Materials (EEM) is an international journal published by Zhengzhou University in collaboration with John Wiley & Sons, Inc. The journal aims to publish high quality research related to materials for energy harvesting, conversion, storage, and transport, as well as for creating a cleaner environment. EEM welcomes research work of significant general interest that has a high impact on society-relevant technological advances. The scope of the journal is intentionally broad, recognizing the complexity of issues and challenges related to energy and environmental materials. Therefore, interdisciplinary work across basic science and engineering disciplines is particularly encouraged. The areas covered by the journal include, but are not limited to, materials and composites for photovoltaics and photoelectrochemistry, bioprocessing, batteries, fuel cells, supercapacitors, clean air, and devices with multifunctionality. The readership of the journal includes chemical, physical, biological, materials, and environmental scientists and engineers from academia, industry, and policy-making.
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