(Invited) Machine Learned Deep Neural Networks to Simulate Raman Spectrum of Defective Graphene Systems

Abstract

Raman spectrum is a common spectroscopic tool used in materials synthesis and characterization. The width and shift of Raman peaks are commonly used in characterization of graphene samples. In this presentation, I will highlight our efforts in using machine learned models to develop an efficient and accurate methodology for simulating accurate spectroscopic signatures in graphene. We are using deep neural networks trained with density functional theory calculations to take advantage of the accuracy of this approach while being able to simulate large atomic systems corresponding to doped graphene layers. We are then able to evaluate the empirical models presently used to assess the defect levels in graphene and compare them to state-of-the-art calculation on such systems.