The paper “MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials” presents a new approach to materials discovery using a Transformer-based model. The authors introduce MatterGPT, which generates solid-state materials with targeted properties by employing a notation system to encode crystal structures. This model allows for inverse design targeting both single and multiple properties simultaneously. The study showcases the potential for generating novel materials with specific properties, aiming to enhance computational material design in energy, electronics, and other fields.
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