Prediction of excitation wavelength of phosphors by using machine learning model

Luminescent materials are the integral part of green revolution helping us in saving the energy. Much effort been made to design and discover the novel phosphors for solid-state lighting. The current paper focuses on the development of machine learning (ML) model based on simple luminescent materials to predict the excitation to the closest possible accuracy using easily accessible key attributes using least absolute shrinkage and selection operator (LASSO) and artificial neural network (ANN) ML approach. These selected attributes expected to correlate with the excitation of material. The style for studying the material property has the potential to turn down the cost and time involved in an Edisonian approach to the lengthy lab experiment to identify excitation.