  This paper highlights a shift in how to approach material generation. Instead
of material-to-material, we propose a language-to-material generation
architecture that utilizes millions of untapped data points. Using a web
scraper to collect crystal text pairs from open-source research papers, a
contrastive model can be trained using a convolutional graph neural network
encoder and a language encoder. This would allow unsupervised zero-shot
classification which can be trained by taking advantage of linguistic
structure. Without any specific training data, an ~82\% accuracy was achieved
and ~75\% accuracy for photocatalyst prediction with an extremely small
dataset. This novel network could ideally be cross-applied to any reaction that
can be described via text, opening completely new methods to think about 3D
chemical framework generation. In the full experiment diffusion models would
likely be incorporated to fully exploit the latent space.
