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AI-enabled materials discovery for advanced ceramic electrochemical cells

Energy and AI 2024年第1期15卷 55-87页

作者： Idris Temitope Bello Ridwan Taiwo Oladapo Christopher Esan Adesola Habeeb Adegoke Ahmed Olanrewaju Ijaola Zheng Li Siyuan Zhao Chen Wang Zongping Shao Meng Ni Department of Building and Real Estate Research Institute for Sustainable Urban Development(RISUD)&Research Institute for Smart Energy(RISE)The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Civil Engineering University of JohannesburgSouth Africa Department of Mechanical Engineering Wichita State UniversityKansasUSA WA School of Mines:Minerals Energy and Chemical EngineeringCurtin UniversityPerthWestern Australia6845Australia

Ceramic electrochemical cells(CECs)are promising devices for clean and efficient energy conversion and storage due to their high energy efficiency,more extended system durability,and less expensive ***,the search for suitable materials with desired properties,including high ionic and electronic conductivity,thermal stability,and chemical compatibility,presents ongoing challenges that impede widespread adoption and further advancement in the *** intelligence(AI)has emerged as a versatile tool capable of enhancing and expediting the materials discovery cycle in CECs through data-driven modeling,simulation,and optimization ***,we comprehensively review the state-of-the-art AI applications for materials design and optimization for CECs,covering various material aspects,database construction,data pre-processing,and AI *** also present some representative case studies of AI-predicted and synthesized materials for CECs and provide insightful highlights about their *** emphasize the main implications and contributions of the AI approach for advancing the CEC technology,such as reducing the trial-and-error experiments,exploring the vast materials space,discovering novel and optimal materials,and enhancing the understanding of the materials-performance *** also discuss the AI approach’s main limitations and future directions for CECs,such as addressing the data and model challenges,improving and extending the AI models and methods,and integrating with other computational and experimental *** conclude by suggesting some potential applications and collaborations for AI in materials design for CECs.

关键词： Ceramic electrochemical cells Artificial intelligence materials design materials optimization materials performance Machine learning

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