Prediction of Quantum Yields of Monolayer WS2 by Machine Learning

Abstract Monolayer transition metal dichalcogenides (1L‐TMDs) exhibits distinct light emissions in the visible range, making them suitable for 2D optoelectronic applications. Photoluminescence quantum yield (PLQY) is a key factor for practical applications of 1L‐TMDs. However, the methods for PLQY measurements of 1L‐TMDs suffer from limitations due to the small sample size and typically low PLQY, which require a complex measurement setup. In this study, machine learning (ML) models are developed to predict the PLQY of monolayer tungsten disulfide (1L‐WS 2 ) using data extracted from 1208 PL spectra and corresponding measurement conditions as the ML training and testing data set. The ML model shows a high accuracy with R 2 value of 0.744 and a mean absolute percentage error of 44% in the prediction of widely ranged PLQYs of 1L‐WS 2 from 0.07% to 38%. This data‐driven prediction not only enables the convenient PLQY estimation of 1L‐TMDs, but also helps in identifying key parameters influencing PLQYs.