Machine learning methods for aerosol synthesis of single-walled carbon nanotubes

This work is devoted to the strategy towards the optimal development of multiparametric process of single-walled carbon nanotube (SWCNT) synthesis. Here, we examine the implementation of machine learning techniques and discuss features of the optimal dataset size and density for aerosol chemical vapor deposition method with a complex carbon source. We employ the dataset of 369 points, comprising synthesis parameters (catalyst amount, temperature, feed of carbon sources) and corresponding carbon nanotube characteristics (yield, quality, structure, optoelectrical figure of merit). Assessing the performance of six machine learning methods on the dataset, we demonstrate Artificial Neural Network to be the most suitable approach to predict the outcome of synthesis processes. We show that even a dataset of 250 points with the inhomogeneous distribution of input parameters is enough to reach an acceptable performance of the Artificial Neural Network, wherein the error is most likely to arise from experimental inaccuracy and hidden uncontrolled variables. We believe our work will contribute to the selection of an appropriate regression algorithm for the controlled carbon nanotube synthesis and further development of an autonomous synthesis system for an “on-demand” SWCNT production.