Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Nate Gruver; Anuroop Sriram; Andrea Madotto; Andrew Gordon Wilson; C. Lawrence Zitnick; Zachary Ulissi

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arXiv · 2402.04379

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Nate Gruver·Anuroop Sriram·Andrea Madotto·Andrew Gordon Wilson·C. Lawrence Zitnick·Zachary Ulissi

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Abstract

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.

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Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ulissi. 2025-07-24. Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. https://arxiv.org/abs/2402.04379

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