Modeling Graphene Extraction Process Using Generative Diffusion Models

Graphene, a two-dimensional material composed of carbon atoms arranged in a hexagonal lattice, possess a unique array of properties that make it a highly sought-after material for a wide range of applications. Its extraction process, a chemical reaction’s result, is represented as an image that shows areas of the synthesized material. Knowing the initial conditions (oxidizer) the synthesis result could be modeled by generating possible visual outcomes. A novel text2image pipeline to generate experimental images from chemical oxidizers is proposed. Key components of a such pipeline are a textual input encoder and a conditional generative model. In this work, the capabilities of certain text model and generative diffusion model are investigated and some conclusions are drawn providing further suggestions for further full text2image pipeline development