Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges
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 m...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Engineered Science Publisher
2020
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| Online Access: | https://hdl.handle.net/1721.1/126055 |
| _version_ | 1826209428267859968 |
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| author | Zhang, Hang Hippalgaonkar, Kedar Buonassisi, Tonio Løvvik, Ole M. Sagvolden, Espen Ding, Ding |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Zhang, Hang Hippalgaonkar, Kedar Buonassisi, Tonio Løvvik, Ole M. Sagvolden, Espen Ding, Ding |
| author_sort | Zhang, Hang |
| collection | MIT |
| description | 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 |
| first_indexed | 2024-09-23T14:22:21Z |
| format | Article |
| id | mit-1721.1/126055 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:22:21Z |
| publishDate | 2020 |
| publisher | Engineered Science Publisher |
| record_format | dspace |
| spelling | mit-1721.1/1260552022-09-29T09:03:00Z Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges Zhang, Hang Hippalgaonkar, Kedar Buonassisi, Tonio Løvvik, Ole M. Sagvolden, Espen Ding, Ding Massachusetts Institute of Technology. Department of Mechanical Engineering 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 Basic Science Center Program for Ordered Energy Conversion of the National Natural Science Foundation of China (No. 51888103) A*Star's Science and Engineering Research Council, on Accelerating Materials Development for Manufacturing (project no: A1898b0043) A*Star's AME Young Independent Research Grant project (no. A1884c0020) 2020-07-02T22:36:08Z 2020-07-02T22:36:08Z 2018-12 2018-12 2020-06-24T18:13:09Z Article http://purl.org/eprint/type/JournalArticle 2578-0654 https://hdl.handle.net/1721.1/126055 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 en https://dx.doi.org/10.30919/ESEE8C209 ES Energy & Environment Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Engineered Science Publisher arXiv |
| spellingShingle | Zhang, Hang Hippalgaonkar, Kedar Buonassisi, Tonio Løvvik, Ole M. Sagvolden, Espen Ding, Ding Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title_full | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title_fullStr | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title_full_unstemmed | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title_short | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges |
| title_sort | machine learning for novel thermal materials discovery early successes opportunities and challenges |
| url | https://hdl.handle.net/1721.1/126055 |
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