  Creation of nanomaterials with specific morphology remains a complex
experimental process, even though there is a growing demand for these materials
in various industry sectors. This study explores the potential of AI to predict
the morphology of nanoparticles within the data availability constraints. For
that, we first generated a new multi-modal dataset that is double the size of
analogous studies. Then, we systematically evaluated performance of classical
machine learning and large language models in prediction of nanomaterial shapes
and sizes. Finally, we prototyped a text-to-image system, discussed the
obtained empirical results, as well as the limitations and promises of existing
approaches.
