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.

中文翻译：

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热超材料的机器学习辅助设计和优化

人工智能 (AI) 实现了以前难以处理的先进材料研究，例如，机器学习 (ML) 已经能够预测一些前所未有的热性能。在这篇综述中，我们首先阐明了支持判别模型和生成模型的方法，以及优化方法的范式。然后，我们提出了一系列案例研究，展示机器学习在热超材料设计中的应用。最后，我们简要讨论了这个快速发展领域的挑战和机遇。特别是，本综述提供了：（1）使用优化算法优化热超材料以实现特定的目标属性。 (2)判别模型与优化算法相结合，提高计算效率。 (3)热超材料结构设计和优化的生成模型。