In this project, we are going to focus on establishing a methodology to leverage advanced machine learning techniques, particularly deep generative models, to predict novel inorganic material compositions. The model will be trained on a large dataset of computational inorganic materials, learning the underlying distribution and optimizing the relevant physical properties in the properly constructed latent space. Once trained, the model can propose new, valid inorganic compositions that are likely to exhibit desirable properties.

Expertise will be gained in the generative diffusion model as specified in https://arxiv.org/abs/2312.03687, and coding with Python valuable for both future PhD studies and industrial positions.