Run-Lin Liu , Jian Wang , Zhong-Hui Shen , Yang Shen
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引用次数: 0
Abstract
Dielectric capacitors, characterized by ultra-high power densities, have been widely used in Internet of Everything terminals and vigorously developed to improve their energy storage performance for the goal of carbon neutrality. With the boom of machine learning (ML) methodologies, Artificial Intelligence (AI) has been deeply integrated into the research and development of dielectric capacitors, including predicting material properties, optimizing material composition and structure, augmenting theoretical knowledge and so on. Through typical application cases, we comprehensively review that AI has greatly broadened the scope of the design and discovery of dielectric capacitors at multiple scales, ranging from atoms/molecules to domains/grains, films/bulks, and devices/systems. Finally, an outlook on potential solutions to current challenges and some novel applications and breakthroughs that AI may facilitate in the field of dielectric capacitors are highlighted.
期刊介绍：
Energy Storage Materials is a global interdisciplinary journal dedicated to sharing scientific and technological advancements in materials and devices for advanced energy storage and related energy conversion, such as in metal-O2 batteries. The journal features comprehensive research articles, including full papers and short communications, as well as authoritative feature articles and reviews by leading experts in the field.
Energy Storage Materials covers a wide range of topics, including the synthesis, fabrication, structure, properties, performance, and technological applications of energy storage materials. Additionally, the journal explores strategies, policies, and developments in the field of energy storage materials and devices for sustainable energy.
Published papers are selected based on their scientific and technological significance, their ability to provide valuable new knowledge, and their relevance to the international research community.
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