High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
Zhong-Hai Ji, Lili Zhang, Dai‐Ming Tang, Chien‐Ming Chen, Torbjörn E. M. Nordling, Zheng-De Zhang, Cui-Lan Ren, Bo Da, Xin Li, Shu-Yu Guo, Chang Liu, Hui–Ming Cheng
Abstract
It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1,280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.