What is it about?
Machine Learning can efficiently aid us in filtering polymers with high thermal conductivity, dramatically accelerating the development of functional materials. We have summarized the essential aspects in the process of using Machine Learning, and have also provided a summary of the critical factors influencing the high thermal conductivity of polymers discovered through Machine Learning to date.
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Why is it important?
The accuracy of Machine Learning requires constant attention. We have summarized the main process of predicting structures through Machine Learning and offered insights into how to improve prediction accuracy. Additionally, we have summarized the key factors to enhance the thermal conductivity of polymers, aiming to provide direct guidance for future research.
Perspectives
I hope this article can provide the most direct aid to those interested in the development of high thermal conductivity polymers, contributing to the research and development of superior functional materials.
Yinglong Hu
University of Science and Technology of China
Read the Original
This page is a summary of: Machine-learning-assisted searching for thermally conductive polymers: A mini review, Journal of Applied Physics, March 2024, American Institute of Physics,
DOI: 10.1063/5.0201613.
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