Two-dimensional (2D) materials have intriguing physical
and chemical
properties, which exhibit promising applications in the fields of
electronics, optoelectronics, as well as energy storage. However,
the controllable synthesis of 2D materials is highly desirable but
remains challenging. Machine learning (ML) facilitates the development
of insights and discoveries from a large amount of data in a short
time for the materials synthesis, which can significantly reduce the
computational costs and shorten the development cycles. Based on this,
taking the 2D material MoS2 as an example, the parameters
of successfully synthesized materials by chemical vapor deposition
(CVD) were explored through four ML algorithms: XGBoost, Support Vector
Machine (SVM), Naïve Bayes (NB), and Multilayer Perceptron
(MLP). Recall, specificity, accuracy, and other metrics were used
to assess the performance of these four models. By comparison, XGBoost
was the best performing model among all the models, with an average
prediction accuracy of over 88% and a high area under the receiver
operating characteristic (AUROC) reaching 0.91. And these findings
showed that the reaction temperature (T) had a crucial
influence on the growth of MoS2. Furthermore, the importance
of the features in the growth mechanism of MoS2 was optimized,
such as the reaction temperature (T), Ar gas flow
rate (R
f), reaction time (t), and so on. The results demonstrated that ML assisted materials
preparation can significantly minimize the time spent on exploration
and trial-and-error, which provided perspectives in the preparation
of 2D materials.