This study predicts the thermoelectric figure of merit (ZT) for defective gamma-graphyne nanoribbons (γ-GYNRs) using binary coding, convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-scale feature fusion. The approach accurately predicts ZT values with only 500 initial structures (3% of 16,512 candidates), achieving an R above 0.91 and a mean absolute error (MAE) of 0.05 to 0.06. The use of artificial feature extraction combined with an attention mechanism reveals that the number and distribution of defects are crucial for achieving high ZT values. γ-GYNRs with moderate and evenly distributed defect count show superior thermoelectric performance. This demonstrates the effectiveness of neural networks in designing low-dimensional materials like γ-GYNRs and offers insights into exploring other materials with excellent thermoelectric properties.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1038/s41598-024-84074-z | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons.
Sci Rep
January 2025
Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.
This study predicts the thermoelectric figure of merit (ZT) for defective gamma-graphyne nanoribbons (γ-GYNRs) using binary coding, convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-scale feature fusion. The approach accurately predicts ZT values with only 500 initial structures (3% of 16,512 candidates), achieving an R above 0.91 and a mean absolute error (MAE) of 0.
View Article and Find Full Text PDFEvaluating the appropriateness of γ-graphyne derivatives as electrode materials for supercapacitors.
Sci Rep
September 2023
Department of Chemistry, Iran University of Science and Technology, P.O. Box, Tehran, 16846-13114, Iran.
DFT calculations were used to study the quantum capacitance of pure, B/Al/Si/N/P-doped, and defective γ-graphyne. Due to the direct relationship between capacitance and electronic states around the Fermi level, structures' electronic properties were evaluated by DOS plots. The results of integrated specific quantum capacitance in the range of water stability potential show an improvement of capacity in each p and n-type doping.
View Article and Find Full Text PDFA ball-milling synthesis of N-graphyne with controllable nitrogen doping sites for efficient electrocatalytic oxygen evolution and supercapacitors.
Dalton Trans
August 2020
Nanomaterials and Solar Energy Conversion Lab, Department of Chemistry, National Institute of Technology, Tiruchirappalli 620 015, India.
Low-cost and efficient multifunctional electrodes play an important part in promoting the practical application of energy conversion and storage. Herein, we report the facile synthesis of N-graphyne, with a novel structure, by one-step ball milling of CaC2 and pyrazine. The accurate doping of nitrogen atoms at the controllable sites of the molecular skeleton of γ-graphyne was achieved using the nitrogenous precursor (pyrazine) as a reactant.
View Article and Find Full Text PDFγ-Graphyne nanotubes as defect-free catalysts of the oxygen reduction reaction: a DFT investigation.
Phys Chem Chem Phys
April 2020
School of Chemistry and Chemical Engineering, University of Jinan, Jinan, Shandong 250022, P. R. China.
Graphyne materials are potential candidates to fabricate low-cost but efficient metal-free oxygen reduction reaction (ORR) electrocatalysts. However, due to the coexistence of sp and sp2 carbon atoms in graphyne, some factors playing important roles in determining the ORR activity have received little attention. In the present paper, we carried out thorough density functional theory (DFT) calculations to study the curvature effect on the ORR activity of γ-graphyne.
View Article and Find Full Text PDFLarge-scale calculations of thermoelectric transport coefficients: a case study of γ-graphyne with point defects.
J Phys Condens Matter
November 2017
Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China.
Defects such as vacancies and impurities could have profound effects on the transport properties of thermoelectric materials. However, it is usually quite difficult to directly calculate the thermoelectric properties of defect-containing systems via first-principles methods since a very large supercell is required. In this work, based on the linear response theory and the kernel polynomial method, we present an efficient approach that can help to calculate the thermoelectric transport coefficients of a large system containing millions of atoms at arbitrary chemical potential and temperature.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!