Machine learning driven synthesis of few-layered WTe2

TL;DR

This paper demonstrates how supervised machine learning can optimize the chemical vapor deposition process to synthesize high-quality 1D WTe2 nanoribbons, providing insights into their growth mechanisms and advancing nanomaterial development.

Contribution

It introduces a supervised ML approach for optimizing the synthesis of 1D WTe2 nanoribbons via CVD, and proposes a growth mechanism model, enhancing material synthesis efficiency.

Findings

ML effectively optimized synthesis parameters for WTe2 NRs

Growth mechanism insights for 1T' WTe2 NRs proposed

ML-guided synthesis accelerates development of 1D nanostructures

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 device 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 the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T' few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.