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DOI	10.1039/d1ee00398d
	Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design
	Niu Z.; Pinfield V.J.; Wu B.; Wang H.; Jiao K.; Leung D.Y.C.; Xuan J.
发表日期	2021
ISSN	17545692
起始页码	2549
结束页码	2576
卷号	14 期号:5
英文摘要	Porous energy materials are essential components of many energy devices and systems, the development of which have been long plagued by two main challenges. The first is the 'curse of dimensionality', i.e. the complex structure-property relationships of energy materials are largely determined by a high-dimensional parameter space. The second challenge is the low efficiency of optimisation/discovery techniques for new energy materials. Digitalisation of porous energy materials is currently being considered as one of the most promising solutions to tackle these issues by transforming all material information into the digital space using reconstruction and imaging data and fusing this with various computational methods. With the help of material digitalisation, the rapid characterisation, the prediction of properties, and the autonomous optimisation of new energy materials can be achieved by using advanced mathematical algorithms. In this paper, we review the evolution of these computational and digital approaches and their typical applications in studying various porous energy materials and devices. Particularly, we address the recent progress of artificial intelligence (AI) in porous energy materials and highlight the successful application of several deep learning methods in microstructural reconstruction and generation, property prediction, and the performance optimisation of energy materials in service. We also provide a perspective on the potential of deep learning methods in achieving autonomous optimisation and discovery of new porous energy materials based on advanced computational modelling and AI techniques. © 2021 The Royal Society of Chemistry.
英文关键词	Deep learning; Digital devices; Metadata; Computational modelling; Curse of dimensionality; Material information; Mathematical algorithms; Microstructural design; Performance optimisation; Property predictions; Typical application; Learning systems; algorithm; artificial intelligence; data set; design method; equipment; modeling; reconstruction
语种	英语
来源期刊	Energy & Environmental Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/190671
作者单位	Department of Chemical Engineering, Loughborough University, Loughborough, United Kingdom; Department of Mechanical Engineering, University of Hong Kong, Hong Kong, Hong Kong; Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Department of Mechanical Engineering, Imperial College London, London, United Kingdom; State Key Laboratory of Engines, Tianjin University, 135 Yaguan Road, Tianjin, 300350, China
推荐引用方式 GB/T 7714	Niu Z.,Pinfield V.J.,Wu B.,et al. Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design[J],2021,14(5).
APA	Niu Z..,Pinfield V.J..,Wu B..,Wang H..,Jiao K..,...&Xuan J..(2021).Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design.Energy & Environmental Science,14(5).
MLA	Niu Z.,et al."Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design".Energy & Environmental Science 14.5(2021).