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

Jiayi Guo, Chunfeng Cui, Tao Ouyang, Juexian Cao, Xiaolin Wei

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jiayi GuoDepartment of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.

Chunfeng CuiDepartment of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.

Tao OuyangDepartment of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.

Juexian CaoDepartment of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China. jxcao@xtu.edu.cn.

Xiaolin WeiDepartment of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China. xlw@xtu.edu.cn.

Funding

National Key Research and Development Program of China 2022YFC2205003National Natural Science Foundation of China 52073243，12204398 and 52303340Science and Technology Program of Hunan Province 2022RC3027the Department of Science and Technology of Hunan Province 2023JJ40617

6 · The paper itself

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 only 500 initial structures (3% of 16,512 candidates), achieving an R

Indexed as

Attention mechanismConvolutional neural networksGraphyne nanoribbonsLong short-term memory networksMulti-scale feature fusionThermoelectric figure of merit

Identifiers

PMID39775093

DOI10.1038/s41598-024-84074-z

PMCPMC11707238

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.