Machine Learning in Solid-State Hydrogen Storage Materials: Challenges and Perspectives.

Autor: Zhou P; College of Materials Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China.; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Zhou Q; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Xiao X; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.; School of Advanced Energy, Sun Yat-Sen University, Shenzhen, 518107, China., Fan X; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China., Zou Y; Guangxi Key Laboratory of Information Materials, Guilin University of Electronic Technology, Guilin, 541004, China., Sun L; Guangxi Key Laboratory of Information Materials, Guilin University of Electronic Technology, Guilin, 541004, China., Jiang J; College of Materials Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China., Song D; College of Materials Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China., Chen L; State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310058, China.; Key Laboratory of Hydrogen Storage and Transportation Technology of Zhejiang Province, Hangzhou, Zhejiang, 310027, China.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2024 Dec 20, pp. e2413430. Date of Electronic Publication: 2024 Dec 20.
DOI: 10.1002/adma.202413430
Abstrakt: Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high-performance solid-state hydrogen storage materials (HSMs). This review summarizes the state-of-the-art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de-/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described. Specific examples include the successful application of ML in titanium-based, rare-earth-based, solid solution, magnesium-based, and complex HSMs, showcasing its role in exploiting composition-structure-property relationships and designing novel HSMs for specific applications. One of the representative ML works is the single-phase Ti-based HSM with superior cost-effective and comprehensive properties, tailored to fuel cell hydrogen feeding system at ambient temperature and pressure through high-throughput composition-performance scanning. More importantly, this review also identifies and critically analyzes the key challenges faced by ML in this domain, including poor data quality and availability, and the balance between model interpretability and accuracy, together with feasible countermeasures suggested to ameliorate these problems. In summary, this work outlines a roadmap for enhancing ML's utilization in solid-state hydrogen storage research, promoting more efficient and sustainable energy storage solutions.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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