  Large language models (LLMs) such as generative pretrained transformers
(GPTs) have shown potential for various commercial applications, but their
applicability for materials design remains underexplored. In this article, we
introduce AtomGPT, a model specifically developed for materials design based on
transformer architectures, to demonstrate the capability for both atomistic
property prediction and structure generation. We show that a combination of
chemical and structural text descriptions can efficiently predict material
properties with accuracy comparable to graph neural network models, including
formation energies, electronic bandgaps from two different methods and
superconducting transition temperatures. Furthermore, we demonstrate that
AtomGPT can generate atomic structures for tasks such as designing new
superconductors, with the predictions validated through density functional
theory calculations. This work paves the way for leveraging LLMs in forward and
inverse materials design, offering an efficient approach to the discovery and
optimization of materials.
