Machine learning for accelerated prediction of lattice thermal conductivity at arbitrary temperature
We propose a neural network model that allows ready and accurate prediction of the lattice thermal conductivities of crystalline materials at arbitrary temperature.
Authors
- Yufeng Luo (ORCID: https://orcid.org/0000-0002-5121-8718)
- Haibin Cao
- Huijun Liu (ORCID: https://orcid.org/0000-0001-5801-7217)
- Mengke Li (ORCID: https://orcid.org/0000-0002-9433-9683)
- Zihe Li (ORCID: https://orcid.org/0000-0002-1053-3826)
- Ying Fang (ORCID: https://orcid.org/0009-0004-3125-5804)
Institutions
- Wuhan University (CN)
Publication Details
- Journal
- Digital Discovery
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1039/d4dd00286e
- Citations
- 7
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.82
Funders
- National Natural Science Foundation of China
- Wuhan University