Rapid and accurate identification of structural and electronic properties of Xenes under different strains via a first-principles machine-learning approach for energy-electronic devices-Reference-Cited by-同舟云学术

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Rapid and accurate identification of structural and electronic properties of Xenes under different strains via a first-principles machine-learning approach for energy-electronic devices

Published:2024-08 Issue: Volume:40 Page:109904
ISSN:2352-4928
Container-title:Materials Today Communications
language:en
Short-container-title:Materials Today Communications

Author:

Wang Guoqing,Liu Rongchao,Gebreslassie Gebrehiwot,Desta Halefom G.,Tian Dong,Lin Bin

Funder

Science and Technology Department of Sichuan Province

Publisher

Elsevier BV

Reference66 articles.

1. High-rate electrochemical energy storage through Li+ intercalation pseudocapacitance;Augustyn;Nat. Mater.,2013

2. Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions;Kang;Nat. Mater.,2023

3. High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network;Zhang;Nat. Commun.,2024

4. Ready-to-transfer two-dimensional materials using tunable adhesive force tapes;Nakatani;Nat. Electron.,2024

5. New developments in gas sensing using various two-dimensional architectural designs;Aftab;Chem. Eng. J.,2023

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