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Thermal insulating thin films are essential for advanced energy and thermal management applications. This presentation focuses on the integration of experimental data and machine learning techniques to design and optimize high-performance thermal insulating thin films. A key aspect of this work is the development of predictive models for interfacial thermal resistance (ITR), which combine experimental databases and machine learning to accurately predict heat flow across material interfaces. Case studies will demonstrate how these models are applied to improve thin-film thermal insulators and identify structural features that enhance their performance. The presentation also explores the extension of these data-driven approaches to amorphous materials, focusing on the structural analysis of amorphous germanium. We uncover the structural factors influencing thermal conductivity in disordered systems using experimental data from frequency-domain thermoreflectance and high-resolution microscopy. These findings illustrate how combining experimental databases with advanced computational techniques not only accelerates the development of thermal insulating materials but also deepens our understanding of heat transport in complex systems. This work bridges experimental and computational methodologies to innovate thermal insulation technologies, offering solutions for energy-efficient and sustainable materials.

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Keyword: Interfacial thermal resistance, machine learning, thermal insulator, thin film

Conference: Nature Conferences-Materials for AI, AI for Materials (2025-02-05 - 2025-02-07)

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Manuscript type: Author's version (Submitted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5363

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Updated at: 2025-03-12 16:30:31 +0900

Published on MDR: 2025-03-12 16:30:31 +0900