Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Jingru Gan, Jingru Gan, , ,
| Summary:
Crystal structure generation is fundamental to materials science, enabling
the discovery of novel materials with desired properties. While existing
approaches leverage Large Language Models (LLMs) through extensive fine-tuning
on materials databases, we show that pre-trained LLMs can inherently generate
novel and stable crystal structures without additional fine-tuning. Our
framework employs LLMs as intelligent proposal agents within an evolutionary
pipeline that guides them to perform implicit crossover and mutation operations
while maintaining chemical validity. We demonstrate that MatLLMSearch achieves
a 78.38% metastable rate validated by machine learning interatomic potentials
and 31.7% DFT-verified stability, outperforming specialized models such as
CrystalTextLLM. Beyond crystal structure generation, we further demonstrate
that our framework adapts to diverse materials design tasks, including crystal
structure prediction and multi-objective optimization of properties such as
deformation energy and bulk modulus, all without fine-tuning. These results
establish our framework as a versatile and effective framework for consistent
high-quality materials discovery, offering training-free generation of novel
stable structures with reduced overhead and broader accessibility.
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