Machine Learning Aided Design and Optimization of Thermal Metamaterials

作者：Chang-Liang Zhu, E. Bamidele, Xiang-Ying Shen, Gui-Mei Zhu, Bao-Wen Li · 发表于：Chemical Reviews · 年份：2024 · DOI：10.1021/acs.chemrev.3c00708 · 被引用次数：166 · 研究领域：Medicine

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.