Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159346
Title: Machine learning driven synthesis of few-layered WTe₂ with geometrical control
Authors: Xu, Manzhang
Tang, Bijun
Lu, Yuhao
Zhu, Chao
Lu, Qianbo
Zhu, Chao
Zheng, Lu
Zhang, Jingyu
Han, Nannan
Fang, Weidong
Guo, Yuxi
Di, Jun
Song, Pin
He, Yongmin
Kang, Lixing
Zhang, Zhiyong
Zhao, Wu
Guan, Cuntai
Wang, Xuewen
Liu, Zheng
Keywords: Engineering::Materials
Engineering::Computer science and engineering
Issue Date: 2021
Source: Xu, M., Tang, B., Lu, Y., Zhu, C., Lu, Q., Zhu, C., Zheng, L., Zhang, J., Han, N., Fang, W., Guo, Y., Di, J., Song, P., He, Y., Kang, L., Zhang, Z., Zhao, W., Guan, C., Wang, X. & Liu, Z. (2021). Machine learning driven synthesis of few-layered WTe₂ with geometrical control. Journal of the American Chemical Society, 143(43), 18103-18113. https://dx.doi.org/10.1021/jacs.1c06786
Project: MOE2018-T3-1-002 
RG161/19
Journal: Journal of the American Chemical Society
Abstract: Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for further study. Here, we report the implementation of supervised machine learning (ML) for the chemical vapor deposition (CVD) synthesis of high-quality quasi-1D few-layered WTe2 NRs. Feature importance analysis indicates that H2 gas flow rate has a profound influence on the formation of WTe2, and the source ratio governs the sample morphology. Notably, the growth mechanism of 1T' few-layered WTe2 NRs is further proposed, which provides new insights for the growth of intriguing 2D and 1D tellurides and may inspire the growth strategies for other 1D nanostructures. Our findings suggest the effectiveness and capability of ML in guiding the synthesis of 1D nanostructures, opening up new opportunities for intelligent materials development.
URI: https://hdl.handle.net/10356/159346
ISSN: 0002-7863
DOI: 10.1021/jacs.1c06786
Rights: © 2021 American Chemical Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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