Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
<|reference_start|>Fine-Tuned Language Models Generate Stable Inorganic Materials as Text: We propose fine-tuning large language models for generation of stable materials. While unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable, with around 90% of sampled structures obeying physical constraints on atom positions and charges. Using energy above hull calculations from both learned ML potentials and gold-standard DFT calculations, we show that our strongest model (fine-tuned LLaMA-2 70B) can generate materials predicted to be metastable at about twice the rate (49% vs 28%) of CDVAE, a competing diffusion model. Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material, infilling of partial structures and text-conditional generation. Finally, we show that language models' ability to capture key symmetries of crystal structures improves with model scale, suggesting that the biases of pretrained LLMs are surprisingly well-suited for atomistic data.<|reference_end|>
@article{gruver2024fine-tuned,
    title={Fine-Tuned Language Models Generate Stable Inorganic Materials as Text},
    author={Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C.
  Lawrence Zitnick, Zachary Ulissi},
    journal={arXiv preprint arXiv:2402.04379},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04379},
    primaryClass={cs.LG cond-mat.mtrl-sci}
}