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Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials

Molybdenum disulfide (MoS(2)) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties...

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Autores principales: Wang, Jingting, Lu, Mingying, Chen, Yongxing, Hao, Guolin, Liu, Bin, Tang, Pinghua, Yu, Lian, Wen, Lei, Ji, Haining
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459608/
https://www.ncbi.nlm.nih.gov/pubmed/37630868
http://dx.doi.org/10.3390/nano13162283
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author Wang, Jingting
Lu, Mingying
Chen, Yongxing
Hao, Guolin
Liu, Bin
Tang, Pinghua
Yu, Lian
Wen, Lei
Ji, Haining
author_facet Wang, Jingting
Lu, Mingying
Chen, Yongxing
Hao, Guolin
Liu, Bin
Tang, Pinghua
Yu, Lian
Wen, Lei
Ji, Haining
author_sort Wang, Jingting
collection PubMed
description Molybdenum disulfide (MoS(2)) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS(2) with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS(2) obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS(2) material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson’s correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS(2) materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS(2) materials with a reduction in the time and cost of trial and error.
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spelling pubmed-104596082023-08-27 Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials Wang, Jingting Lu, Mingying Chen, Yongxing Hao, Guolin Liu, Bin Tang, Pinghua Yu, Lian Wen, Lei Ji, Haining Nanomaterials (Basel) Article Molybdenum disulfide (MoS(2)) is a layered transition metal-sulfur compound semiconductor that shows promising prospects for applications in optoelectronics and integrated circuits because of its low preparation cost, good stability and excellent physicochemical, biological and mechanical properties. MoS(2) with high quality, large size and outstanding performance can be prepared via chemical vapor deposition (CVD). However, its preparation process is complex, and the area of MoS(2) obtained is difficult to control. Machine learning (ML), as a powerful tool, has been widely applied in materials science. Based on this, in this paper, a ML Gaussian regression model was constructed to explore the growth mechanism of MoS(2) material prepared with the CVD method. The parameters of the regression model were evaluated by combining the four indicators of goodness of fit (r2), mean squared error (MSE), Pearson correlation coefficient (p) and p-value (p_val) of Pearson’s correlation coefficient. After comprehensive comparison, it was found that the performance of the model was optimal when the number of iterations was 15. Additionally, feature importance analysis was conducted on the growth parameters using the established model. The results showed that the carrier gas flow rate (Fr), molybdenum sulfur ratio (R) and reaction temperature (T) had a crucial impact on the CVD growth of MoS(2) materials. The optimal model was used to predict the size of molybdenum disulfide synthesis under 185,900 experimental conditions in the simulation dataset so as to select the optimal range for the synthesis of large-size molybdenum disulfide. Furthermore, the model prediction results were verified through literature and experimental results. It was found that the relative error between the prediction results and the literature and experimental results was small. These findings provide an effective solution to the preparation of MoS(2) materials with a reduction in the time and cost of trial and error. MDPI 2023-08-09 /pmc/articles/PMC10459608/ /pubmed/37630868 http://dx.doi.org/10.3390/nano13162283 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jingting
Lu, Mingying
Chen, Yongxing
Hao, Guolin
Liu, Bin
Tang, Pinghua
Yu, Lian
Wen, Lei
Ji, Haining
Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title_full Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title_fullStr Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title_full_unstemmed Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title_short Machine Learning-Assisted Large-Area Preparation of MoS(2) Materials
title_sort machine learning-assisted large-area preparation of mos(2) materials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459608/
https://www.ncbi.nlm.nih.gov/pubmed/37630868
http://dx.doi.org/10.3390/nano13162283
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