High-throughput screening and machine learning for the efficient growth of high-quality single-wall

• 作 者: Zhong-Hai Ji Lili Zhang Dai-Ming Tang Chien-Ming Chen Torbj(o)rn E.M.Nordling Zheng-De Zhang Cui-Lan Ren Bo Da Xin Li Shu-Yu Guo Chang Liu Hui-Ming Cheng
• 机 构: Shenyang National Laboratory for Materials Science,Institute of Metal Research (IMR),Chinese Academy
• 出 处: 纳米研究（英文版）
• 发表日期: 2021年12期
• 关键词: single-wall carbon nanotube high throughput machine learning optimization chemic

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 leaming 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 (/G//D) 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.