Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques-Reference-Cited by-同舟云学术

Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques

Published:2023 Issue:11 Volume:11 Page:5801-5810
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Qin Guangzhao¹²^ORCID,Wei Yi¹^ORCID,Yu Linfeng¹,Xu Jinyuan¹,Ojih Joshua²,Rodriguez Alejandro David²,Wang Huimin¹²³^ORCID,Qin Zhenzhen⁴^ORCID,Hu Ming²^ORCID

Affiliation:

1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, P. R. China

2. Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA

3. Hunan Key Laboratory for Micro-Nano Energy Materials & Device and School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, Hunan, China

4. International Laboratory for Quantum Functional Materials of Henan, School of Physics and Engineering, Zhengzhou University, Zhengzhou 450001, China

Abstract

The well-trained machine learning models successfully capture the inherent correlation between fundamental properties and thermal conductivity for different types of materials, providing powerful tool for advanced thermal materials screening.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body

National Science Foundation

University of South Carolina

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D2TA08721A