Experimentally validated machine learning predictions of ultralow thermal conductivity for SnSe materials
Author:
Affiliation:
1. Department of Chemistry, University of Waterloo, 200 University Ave W, Waterloo, ON, Canada
2. Department of Chemistry, Hunter College, City University of New York, New York 10065, USA
Abstract
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Royal Society of Chemistry (RSC)
Subject
Materials Chemistry,General Chemistry
Link
http://pubs.rsc.org/en/content/articlepdf/2023/TC/D3TC01450A
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1. Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset;Chemistry of Materials;2024-07-03
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