Machine Learning DFT-Based Approach to Predict the Electrical Properties of Tin Oxide Materials-Reference-Cited by-同舟云学术

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Machine Learning DFT-Based Approach to Predict the Electrical Properties of Tin Oxide Materials

Published:2023-11-15 Issue: Volume:2 Page:127
ISSN:
Container-title:The 10th International Electronic Conference on Sensors and Applications
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Author:

Ferhati Hichem¹²,Berghout Tarek³,Benyahia Abderraouf¹,Djeffal Faycal¹

Affiliation:

1. Laboratoire Electronique Avancée (LEA), Department of Electronics, University of Batna 2, Batna 05000, Algeria

2. Institut Sciences Techniques Appliquées (ISTA), University of Larbi Ben M’hidi, Oum El Bouaghi 04000, Algeria

3. Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/58/1/127/pdf

Reference6 articles.

1. V-Shaped Tin Oxide Nanostructures Featuring a Broad Photocurrent Signal: An Effective Visible Light-Driven Photocatalyst;Wang;Small,2006

2. Tin oxide for optoelectronic, photovoltaic and energy storage devices: A review;Dalapati;J. Mater. Chem. A,2021

3. Towards improved efficiency of SnS solar cells using back grooves and strained-SnO2 buffer layer: FDTD and DFT calculations;Ferhati;J. Phys. Chem. Solids,2023

4. Experimental Investigation and DFT Study of Tin-Oxide for Its Application as Light Absorber Layer in Optoelectronic Devices;Kumar;IEEE Access,2023

5. Partially Hidden Markov Chain Multivariate Linear Autoregressive model: Inference and forecasting—Application to machine health prognostics;Dama;Mach Learn,2023

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