Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds-Reference-Cited by-同舟云学术

Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds

Published:2022-06-29 Issue:7 Volume:5 Page:8913-8922
ISSN:2574-0962
Container-title:ACS Applied Energy Materials
language:en
Short-container-title:ACS Appl. Energy Mater.

Author:

Bhattacharjee Dipanwita¹,Kundavu Krishnaraj¹,Saraswat Deepanshi¹,Raghuvanshi Parul R.¹,Bhattacharya Amrita¹^ORCID

Affiliation:

1. Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology, Bombay, Powai 400076, Mumbai, Maharashtra, India

Funder

Science and Engineering Research Board

Board of Research in Nuclear Sciences

Department of Science and Technology, Ministry of Science and Technology

Indian Institute of Technology Bombay

Publisher

American Chemical Society (ACS)

Subject

Electrical and Electronic Engineering,Materials Chemistry,Electrochemistry,Energy Engineering and Power Technology,Chemical Engineering (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsaem.2c01400

Reference58 articles.

1. Complex thermoelectric materials

2. Cooling, Heating, Generating Power, and Recovering Waste Heat with Thermoelectric Systems

3. Efficient dopants for ZrNiSn-based thermoelectric materials

4. Coupling the High-Throughput Property Map to Machine Learning for Predicting Lattice Thermal Conductivity

5. High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model

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