Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films

Abstract

A machine learning technique, namely, support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive data set describing the influence of synthesis parameters (temperature and CO₂ concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the data set shows principal applicability of the method for refining synthesis conditions toward the advanced optoelectronic performance of multiparameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl₄ results in the equivalent sheet resistance of 39 Ω/□-one of the lowest values achieved so far for SWCNT films.