Machine learning driven synthesis of few-layered WTe2

Understand

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.