Machine learning driven synthesis of few-layered WTe2

by   Manzhang Xu, et al.

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.



There are no comments yet.


page 8

page 10

page 13

page 17


Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials ...

Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning

Predictive materials synthesis is the primary bottleneck in realizing ne...

Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

Generating sub-optimal synthesis transformation sequences ("synthesis re...

A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data

Understanding the structure and mineralogical composition of a region is...

Rapid Bayesian optimisation for synthesis of short polymer fiber materials

The discovery of processes for the synthesis of new materials involves m...

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

One of the principal uses of physical-space sensors in public safety app...

Gaussian Material Synthesis

We present a learning-based system for rapid mass-scale material synthes...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.