Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control

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 H-2 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.

Automated summary

The study demonstrates the application of supervised machine learning in guiding the synthesis of high-quality quasi-1D WTe2 nanoribbons through chemical vapor deposition, uncovering the profound influence of H2 gas flow rate and source ratio on the formation and morphology of WTe2. The proposed growth mechanism of 1T' few-layered WTe2 nanoribbons offers new insights for the growth of intriguing 2D and 1D tellurides, showcasing the potential of machine learning in guiding the synthesis of 1D nanostructures and opening up new opportunities for intelligent materials development.