Prediction of the Size-Dependent Raman Shift of Semiconductor Nanomaterials via Deep Learning

Abstract

Raman spectroscopy can characterize size-related properties of semiconductor nanomaterials according to the change of Raman shift. When limited to physical mechanisms, it is often difficult to predict the size-dependent Raman shift of semiconductor nanomaterials. To predict the size-dependent Raman shift more accurately and efficiently, a simple and effective method was created, demonstrated, and achieved via the deep learning model. The deep learning model is implemented by multi-layer perceptron. For size-dependent Raman shifts of three common semiconductor nanomaterials (InP, Si, CeO2), the prediction error was 1.47%, 1.18%, and 0.58%, respectively. The research has practical value in material characterization and related engineering applications, where physical mechanisms are not the focus and building predictive models quickly is key.