化学
超材料
生化工程
纳米技术
光电子学
物理
工程类
材料科学
作者
Changliang Zhu,Emmanuel Anuoluwa Bamidele,Xiangying Shen,Guimei Zhu,Baowen Li
标识
DOI:10.1021/acs.chemrev.3c00708
摘要
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
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