Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

TL;DR

This paper presents a deep learning approach using CNNs and diffusion models to enhance Raman spectroscopy analysis for rapid, accurate identification of 2D materials, overcoming data limitations and enabling automated classification.

Contribution

It introduces a novel combination of Denoising Diffusion Probabilistic Models with CNNs for improved spectral data augmentation and classification of 2D materials.

Findings

Achieved 100% classification accuracy with the proposed method.

Demonstrated the effectiveness of DDPM in augmenting limited spectral data.

Showed deep learning can enable rapid, automated analysis of Raman spectra.

Abstract

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis.