Leveraging Deep Neural Networks and Density Functional Theory to guide two-dimensional material synthesis using Chemical Vapor Deposition.

The computational Condensed Matter community has made many strides in recent years towards the discovery of new two-dimensional (2D) materials, utilizing Density Functional Theory (DFT). With the theoretical discovery of these materials, experimentalists must then devise new techniques for efficiently synthesizing said materials. In recent years, modern synthesis approaches have begun to utilize Bayesian Optimization (BO) to guide experimental growth parameter convergence. Unfortunately, given the computational costs, this approach limits the amount of data than can be utilized for model convergence and it requires unguided sample collection to initially train the model. Given the large parameter space for experimental synthesis of 2D materials, we instead propose employing a Deep Neural Network (DNN) to reduce computational costs, where the initial sample collection for training the model is derived using DFT, and the model is guided by Time-Dependent DFT (TD-DFT) computationally derived Raman spectra through, Placzek approximations, of the desired 2D material. In this presentation, we will discuss the TD-DFT and Neural Network computational techniques used, the preliminary results of Chemical Vapor Deposition (CVD) synthesis of 2D materials, and the plans for future work--to include source sharing and further optimizations to the technique.