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dc.contributor.authorXu, Manzhangen_US
dc.contributor.authorTang, Bijunen_US
dc.contributor.authorLu, Yuhaoen_US
dc.contributor.authorZhu, Chaoen_US
dc.contributor.authorLu, Qianboen_US
dc.contributor.authorZhu, Chaoen_US
dc.contributor.authorZheng, Luen_US
dc.contributor.authorZhang, Jingyuen_US
dc.contributor.authorHan, Nannanen_US
dc.contributor.authorFang, Weidongen_US
dc.contributor.authorGuo, Yuxien_US
dc.contributor.authorDi, Junen_US
dc.contributor.authorSong, Pinen_US
dc.contributor.authorHe, Yongminen_US
dc.contributor.authorKang, Lixingen_US
dc.contributor.authorZhang, Zhiyongen_US
dc.contributor.authorZhao, Wuen_US
dc.contributor.authorGuan, Cuntaien_US
dc.contributor.authorWang, Xuewenen_US
dc.contributor.authorLiu, Zhengen_US
dc.identifier.citationXu, 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.
dc.description.abstractReducing 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.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.relation.ispartofJournal of the American Chemical Societyen_US
dc.rights© 2021 American Chemical Society. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMachine learning driven synthesis of few-layered WTe₂ with geometrical controlen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchCNRS International NTU THALES Research Alliancesen_US
dc.subject.keywordsChemical Vapor Depositionen_US
dc.description.acknowledgementThe authors gratefully acknowledge financial support by National Key Research and Development Program of China (2020YFB2008501 and 2019YFC1520900), the National Natural Science Foundation of China (61974120, 11904289, 61701402, and 61804125), Key Research and Development Program of Shaanxi Province (2020ZDLGY04-08, 2020GXLH-Z-027, 2021JZ-43), the Key Program for International Science and Technology Cooperation Projects of Shaanxi Province (2018KWZ-08), the Natural Science Foundation of Shaanxi Province (2019JQ-613), the Natural Science Foundation of Ningbo (202003N4003), the Fundamental Research Funds for the Central Universities (3102019PY004, 31020190QD010, and 3102019JC004), and the start-up funds from Northwestern Polytechnical University. The authors also acknowledge the support from Ministry of Education, Singapore, under its AcRF Tier 3 Programme "Geometrical Quantum Materials" (MOE2018-T3-1-002) and AcRF Tier 1 RG161/19.en_US
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