Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning-Reference-Cited by-同舟云学术

Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning

Published:2020-12-23 Issue:1 Volume:125 Page:435-450
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Loftis Christian¹,Yuan Kunpeng²³,Zhao Yong¹^ORCID,Hu Ming²^ORCID,Hu Jianjun¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29201, United States

2. Department of Mechanical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States

3. Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China

Funder

Office of Integrative Activities

U.S. Department of Energy

National Science Foundation

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.0c08103

Reference52 articles.

1. Model for Lattice Thermal Conductivity at Low Temperatures

2. Machine learning models for the lattice thermal conductivity prediction of inorganic materials

3. Examining the Callaway model for lattice thermal conductivity

4. Improved Callaway model for lattice thermal conductivity

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