# Fileset

[TACT2025-Abstract_Wu.pdf](https://mdr.nims.go.jp/filesets/5344f2ac-55a6-460a-94ee-14926dca3a8b/download)

## Creator

[Yen-Ju Wu](https://orcid.org/0000-0003-2647-3407), [Michiko Sasaki](https://orcid.org/0000-0002-2336-5788), [Masahiro Goto](https://orcid.org/0000-0002-1003-2781), [Yibin Xu](https://orcid.org/0000-0001-8600-8748)

## Rights

[In Copyright](http://rightsstatements.org/vocab/InC/1.0/)

## Other metadata

[Data-Driven Design of Multi-Element Thermal Insulating Thin Films for Advanced Thermal Management](https://mdr.nims.go.jp/datasets/a0f0f97b-b683-4260-a749-9c1e9a5af72a)

## Fulltext

TACT 2025 International Thin Films Conference Oct. 26–29, 2025, National Taipei University of Technology, Taipei, Taiwan Data-Driven Design of Multi-Element Thermal Insulating Thin Films for Advanced Thermal Management Yen-Ju Wu1†*, Michiko Sasaki2, Masahiro Goto2, Yibin Xu1  1Data-driven Inorganic Materials Group, Center for Basic Research on Materials (CBRM), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan 2Thermal Energy Materials Group, Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan  †Presenter: Wu *Corresponding author’s e-mail: Wu.YenJu@nims.go.jp  ABSTRACT  The design of thermal insulating coatings for advanced energy and electronic applications requires a deep understanding of both intrinsic and interfacial thermal transport. In this study, we present a data-driven workflow that integrates interfacial thermal resistance (ITR) modeling, low thermal conductivity screening, and inverse materials design for thin-film applications. We first introduce our previously developed ITR prediction framework, built upon a curated experimental ITR database [1] and machine learning models [2], which enables the estimation of ITR based on interfacial chemical and structural information. Using this framework, we designed thermal insulating thin films with ultra-low thermal conductivities [3,4]. Building on this foundation, we developed predictive models for bulk thermal conductivity using periodic descriptors [5], trained on inorganic compounds from the AtomWork-Adv. (AWA) database [6]. These compact and chemically meaningful descriptors enable efficient learning, achieving a prediction accuracy of R² ~0.8 for bulk thermal conductivity. Applying these models, we screened over 150,000 known materials in AWA and explored potential multi-element (>5) compositions for low thermal conductivity. While none of the screened materials fully satisfied practical constraints such as non-toxicity and low cost, the approach identified unexplored compositional systems worthy of further investigation. Experimental validation of these candidates is currently underway in collaboration with thin-film fabrication teams. This integrated pipeline—combining ITR modeling, bulk thermal property prediction, and multi-element composition design—offers a promising pathway toward the development of next-generation thermal management materials.   Keywords: thermal insulating films, thermal conductivity, periodic descriptors, multi-element materials  Acknowledgment: This research was supported by Japan Science and Technology Agency (JST) CREST (JPMJCR21O2). A part of this work was also supported by Grant-in-Aid for Scientific Research (C) 24K07349 and 25K08337 from the Japan Society for the Promotion of Science (JSPS).  REFERENCES  [1] Y.-J. Wu, T. Zhan, Z. Hou, L. Fang, Y. Xu, Scientific Data, 7, (2020) 36 [2] Y.-J. Wu, L. Fang, Y. Xu, npj Computational Materials 5, (2019) 56 [3] Y.-J. Wu, M. Sasaki, M. Goto, L. Fang, Y. Xu, ACS Applied Nano Materials, 1, (2018) 3355-3363 [4] Y. J. Wu, Y. Xu, Micromachines (Basel), 14, (2023)  [5] Y.-J. Wu et al., Science and Technology of Advanced Materials: Methods, (2025) 2513218 [6] AtomWork-Adv., NIMS, 2018. Available from: https://atomwork-adv.nims.go.jp/  Data-Driven Design of Multi-Element Thermal Insulating Thin Films for Advanced Thermal Management Yen-Ju Wu1†*, Michiko Sasaki2, Masahiro Goto2, Yibin Xu1