Machine learning for thermal transport.

The document discusses the integration of machine learning (ML) into thermal transport research, highlighting its transformative impact on understanding and controlling heat transfer processes. It features 31 papers categorizing ML applications into machine learning potentials, predicting thermal properties, design and optimization, data analysis, and tutorials. ML has enabled accurate simulations, precise property predictions, innovative system designs, and efficient data analysis in thermal transport research, showcasing the potential for further advancements in the field. Despite challenges like model transferability, data scarcity, and interpretability, the document emphasizes the promising future of ML in advancing thermal science and engineering. [Extracted from the article]