锂和非锂先进电池化学计算材料的发现和发展

IF 2.9 Q2 ELECTROCHEMISTRY
Henu Sharma, Aqsa Nazir, Arvind Kasbe, Prathamesh Kekarjawlekar, Kajari Chatterjee, Saeme Motevalian, Ana Claus, Viswesh Prakash, Sagnik Acharya, Kisor K. Sahu
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引用次数: 0

摘要

自19世纪发现电池以来,其迷人的物理和化学特性导致了对其合成和制造的大量研究。尽管锂离子电池对人类文明至关重要,但由于锂资源的地理分布以及电池能量密度、性能和可靠性等问题的内在限制,锂离子电池仍不能满足日益增长的能源存储需求。因此,非锂离子电池正成为越来越受欢迎的替代品。设计具有理想性能的新材料对于更快地过渡到绿色能源生态系统至关重要。钠离子、钾离子、镁离子、锌离子、铝离子等电池被认为是最具吸引力和前景的电池。本文涵盖了所有这些锂电池、非锂电池和金属-空气电池的化学性质。最近,计算筛选已被证明是一种有效的工具,可以加速发现所有这些细胞类型的活性物质。第一性原理方法,如密度泛函理论、分子动力学和蒙特卡罗模拟,已经成为电池系统初步理论分析的成熟技术。这些计算方法产生的大量数据可能在人工智能和机器学习技术的培训和验证中非常有用,以减少发现先进材料和最终产品开发所需的时间和资本支出。本文综述了这些技术的应用以及计算方法在发现和开发先进电池化学方面的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational materials discovery and development for Li and non-Li advanced battery chemistries
Since the discovery of batteries in the 1800s, their fascinating physical and chemical pro­perties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, com­putational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techni­ques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and vali­dating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product develop­ment. This review aims to summarize the application of these techniques and the recent deve­lopments in computational methods to discover and develop advanced battery chemistries.
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来源期刊
CiteScore
3.60
自引率
27.30%
发文量
90
审稿时长
6 weeks
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