dc.contributor.author | Zhang, Hang | |
dc.contributor.author | Hippalgaonkar, Kedar | |
dc.contributor.author | Buonassisi, Tonio | |
dc.contributor.author | Løvvik, Ole M. | |
dc.contributor.author | Sagvolden, Espen | |
dc.contributor.author | Ding, Ding | |
dc.date.accessioned | 2020-07-02T22:36:08Z | |
dc.date.available | 2020-07-02T22:36:08Z | |
dc.date.issued | 2018-12 | |
dc.date.submitted | 2018-12 | |
dc.identifier.issn | 2578-0654 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/126055 | |
dc.description.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 | en_US |
dc.description.sponsorship | Basic Science Center Program for Ordered Energy Conversion of the National Natural Science Foundation of China (No. 51888103) | en_US |
dc.description.sponsorship | A*Star's Science and Engineering Research Council, on Accelerating Materials Development for Manufacturing (project no: A1898b0043) | en_US |
dc.description.sponsorship | A*Star's AME Young Independent Research Grant project (no. A1884c0020) | en_US |
dc.language.iso | en | |
dc.publisher | Engineered Science Publisher | en_US |
dc.relation.isversionof | https://dx.doi.org/10.30919/ESEE8C209 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges | en_US |
dc.type | Article | en_US |
dc.identifier.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 | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | en_US |
dc.relation.journal | ES Energy & Environment | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2020-06-24T18:13:09Z | |
dspace.date.submission | 2020-06-24T18:13:11Z | |
mit.journal.volume | 2 | en_US |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Complete | |