Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors-Reference-Cited by-同舟云学术

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Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors

Published:2021-06-07 Issue:18 Volume:121 Page:
ISSN:0020-7608
Container-title:International Journal of Quantum Chemistry
language:en
Short-container-title:Int J Quantum Chem

Author:

Wan Zhongyu¹²^ORCID,Wang Quan‐De¹,Liu Dongchang³,Liang Jinhu⁴

Affiliation:

1. Low Carbon Energy Institute and School of Chemical Engineering, China University of Mining and Technology Xuzhou China

2. School of Science, City University of Hong Kong Hong Kong China

3. School of Science, Xi'an Polytechnic University Xi'an China

4. School of Environment and Safety Engineering, North University of China Taiyuan China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

Physical and Theoretical Chemistry,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qua.26752

Reference37 articles.

1. Achieving high-performance p-type SmMg2Bi2 thermoelectric materials through band engineering and alloying effects

2. Annealing of Strontium Titanate Based Thermoelectric Materials by Graphite: Mechanistic Analysis by Spectroscopic and Chromatographic Techniques

3. Intrinsically self-healable, stretchable thermoelectric materials with a large ionic Seebeck effect

4. Tailoring nanostructured NbCoSn-based thermoelectric materials via crystallization of an amorphous precursor

5. Low-cost p-type Bi2Te2.7Se0.3 zone-melted thermoelectric materials for solid-state refrigeration

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1. Bandgap prediction of non-metallic crystals through machine learning approach;Journal of Physics: Condensed Matter;2024-05-14

2. Artificial Intelligence for Learning Material Synthesis Processes of Thermoelectric Materials;Chemistry of Materials;2023-09-21

3. A Critical Review of Machine Learning Techniques on Thermoelectric Materials;The Journal of Physical Chemistry Letters;2023-02-10

4. Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit;ACS Applied Materials & Interfaces;2022-12-06

5. Bandgap analysis of transition-metal dichalcogenide and oxide via machine learning approach;Journal of Physics and Chemistry of Solids;2022-12

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