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在全球向可持续能源转型的浪潮中,氢能凭借环保、零排放和高效的优点,成为重塑能源格局的关键力量。然而,氢能的大规模应用面临着储存和运输的技术难题。目前,虽然研究聚焦在开发高效储氢材料,但传统材料设计多依赖经验和试错法。近年来,人工智能技术在加速材料发现和优化方面崭露头角。通过高通量筛选和机器学习在固态储氢材料中的协同应用,能精准预测储氢性能、优化材料表现,加速创新储氢材料的研发。人工智能在金属氢化物、合金、碳材料等多种储氢材料的开发中发挥着重要作用。此外,地下储氢作为可扩展的可再生能源存储方案,借助人工智能能优化存储参数和预测性能。分析现有储氢方法的局限和人工智能驱动方法的潜力,有助于发现和优化高性能储氢材料,推动全球能源的可持续发展和技术创新。
期刊
Energy&Environmental Materials
标题
Artificial Intelligence-Driven Innovations in Hydrogen Storage Technology
作者
Yusong Ding, Lele Tong, Xiaolin Liu, Ying Liu, Yan Zhao
摘要
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.
原文链接
https://onlinelibrary.wiley.com/doi/10.1002/eem2.70041
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