MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials

Understand

Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science.

• Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficiency, architectural constraints and restricted open-source availability.
• The representation of crystal structures using the SLICES (Simplified Line-Input Crystal-Encoding System) notation as a string of characters enables the use of state-of-the-art natural language processing models, such as Transformers, for crystal design.
• Drawing inspiration from the success of GPT models in generating coherent text, we trained a generative Transformer on the next-token prediction task to generate solid-state materials with targeted properties.