氢气储存
可解释性
材料科学
吞吐量
计算机科学
工艺工程
系统工程
纳米技术
机器学习
工程类
电信
复合材料
无线
合金
作者
Panpan Zhou,Qianwen Zhou,Xuezhang Xiao,Xiulin Fan,Yongjin Zou,Lixian Sun,Jinghua Jiang,Dan Song,Lixin Chen
标识
DOI:10.1002/adma.202413430
摘要
Abstract 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.
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