A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons

A cutting-edge neural network approach for predicting the thermoelectric efficiency of defective gamma-graphyne nanoribbons

Abstract 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 on...

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Bibliographic Details
Main Authors: Jiayi Guo, Chunfeng Cui, Tao Ouyang, Juexian Cao, Xiaolin Wei
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Thermoelectric figure of merit
Graphyne nanoribbons
Convolutional neural networks
Long short-term memory networks
Multi-scale feature fusion
Attention mechanism
Online Access:https://doi.org/10.1038/s41598-024-84074-z
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https://doi.org/10.1038/s41598-024-84074-z

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