钠离子和钾离子电池密度泛函理论研究进展:进展、挑战与展望

IF 31.6 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mei Yang , Shuling Chen , Yunqi Jia , Liuzhang Ouyang
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引用次数: 0

摘要

钠离子电池(SIBs)和钾离子电池(PIBs)因其原料丰富、成本低、安全性高而成为大规模储能的有希望的替代方案。密度泛函理论(DFT)由于实验研究成本高、耗时长,已成为筛选电极材料的关键技术。因此,我们对DFT在sib和pib研究中的应用进行了系统的讨论。本文首先概述了DFT过程和相关概念,包括理论发展、计算内容、相关软件和边界条件描述。然后阐明sib和pib的工作原理和主要挑战。第三部分重点介绍了DFT的三种主要实现。首先,通过结构优化、缺陷设计和复合材料相分析,通过降低能量来提高结构稳定性。其次,通过考察分子轨道、电荷密度、能带结构和态密度,阐明了电子结构修饰与电池性能之间的关系。第三,优异的反应动力学是建立在确定最佳离子迁移途径和最小能量势垒的基础上的。最后,为了解决DFT固有的局限性,特别是在计算效率、原子和电子的限制尺度以及电化学条件的精确建模方面,建议将DFT与机器学习和其他计算方法相结合。这种结合的方法利用互补的优势来提高效率和扩大模拟规模。本文为高性能sib和pib的研究提供了有价值的参考,促进了更高效的储能解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The review of sodium and potassium-ion battery advances in density functional theory: Progresses, challenges and prospects
Sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs) have emerged as promising alternatives for large-scale energy storage due to their abundant raw materials, low cost, and high safety. Density functional theory (DFT) has become a crucial technique for screening electrode materials due to the high cost and time-consuming nature of experimental research. Therefore, we present a systematic discussion of DFT applications in SIBs and PIBs studies. This review first outlines DFT processes and related concepts, including theoretical development, computational content, relevant software, and boundary condition description. It then clarifies the working principles and key challenges of SIBs and PIBs. The third part focuses on three primary implements of DFT. First, structural stability is enhanced through energy reduction, as demonstrated by structural optimization, defect design, and composite phase analysis. Second, the relationship between electronic structure modifications and battery performance is elucidated by examining molecular orbitals, charge density, band structures, and density of states. Third, superior reaction kinetics are predicated upon the identification of optimal ion migration pathways and minimal energy barriers. Finally, to address the inherent limitations of DFT, particularly in computational efficiency, the restricted scale of atoms and electrons, and the accurate modelling of electrochemical conditions, it is recommended to integrate DFT with machine learning and other computational approaches. This combined approach leverages complementary strengths to enhance efficiency and expand simulation scale. This review serves as a valuable reference for research on superior performance SIBs and PIBs, promoting more efficient energy storage solutions.
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来源期刊
Materials Science and Engineering: R: Reports
Materials Science and Engineering: R: Reports 工程技术-材料科学:综合
CiteScore
60.50
自引率
0.30%
发文量
19
审稿时长
34 days
期刊介绍: Materials Science & Engineering R: Reports is a journal that covers a wide range of topics in the field of materials science and engineering. It publishes both experimental and theoretical research papers, providing background information and critical assessments on various topics. The journal aims to publish high-quality and novel research papers and reviews. The subject areas covered by the journal include Materials Science (General), Electronic Materials, Optical Materials, and Magnetic Materials. In addition to regular issues, the journal also publishes special issues on key themes in the field of materials science, including Energy Materials, Materials for Health, Materials Discovery, Innovation for High Value Manufacturing, and Sustainable Materials development.
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