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.

To achieve high-energy-density and safe lithium-metal batteries (LMBs), solid-state electrolytes (SSEs) that exhibit fast Li-ion conductivity and good stability against lithium metal are of great importance. This study presents a systematic exploration of selenide-based materials as potential SSE candidates. Initially, Li₈SeN₂ and Li₇PSe₆ were selected from 25 ternary selenides based on their ability to form stable interfaces with lithium metal. Subsequently, their favorable electronic insulation and mechanical properties were verified. Furthermore, extensive theoretical investigations were conducted to elucidate the fundamental mechanisms underlying Li-ion migration in Li₈SeN₂, Li₇PSe₆, and derived Li₆PSe₅X (X = Cl, Br, I). Notably, the highly favorable Li-ion conduction mechanism of vacancy diffusion was identified in Li₆PSe₅Cl and Li₇PSe₆, which exhibited remarkably low activation energies of 0.21 and 0.23 eV, and conductivity values of 3.85 × 10⁻² and 2.47 × 10⁻² S cm⁻¹ at 300 K, respectively. In contrast, Li-ion migration in Li₈SeN₂ was found to occur via a substitution mechanism with a significant diffusion energy barrier, resulting in a high activation energy and low Li-ion conductivity of 0.54 eV and 3.6 × 10⁻⁶ S cm⁻¹, respectively. Throughout this study, it was found that the ab initio molecular dynamics and nudged elastic band methods are complementary in revealing the Li-ion conduction mechanisms. Utilizing both methods proved to be efficient, as relying on only one of them would be insufficient. The discoveries made and methodology presented in this work lay a solid foundation and provide valuable insights for future research on SSEs for LMBs.