Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges
Name
1901.05801.pdf
Description
Accepted version
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
8e98581edb85ee16733ff73000b76f35
Author(s)
Zhang, Hang
Hippalgaonkar, Kedar
Buonassisi, Tonio
Løvvik, Ole M.
Sagvolden, Espen
Ding, Ding
Date Issued
December 2018
Journal
ES Energy & Environment
Publisher
Engineered Science Publisher
Citation
Zhang, Hang et al., "Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges." ES Energy & Environment 2 (December 2018): p. 1-8 doi. 10.30919/esee8c209 ©2018 Authors
Version
Author's final manuscript
Abstract
High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in part due to the advances in computational and experimental methods in obtaining thermal properties of materials. In this paper, we provide a current overview of some of the recent work and highlight the challenges and opportunities that are ahead of us in this field. In particular, we focus on the use of machine learning and high-throughput methods for screening of thermal conductivity for compounds, composites and alloys as well as interfacial thermal conductance. These new tools have brought about a feedback mechanism for understanding new correlations and identifying new descriptors, speeding up the discovery of novel thermal functional materials. ©2018
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.30919/ESEE8C209
