Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver Anuroop Sriram Andrea Madotto Andrew Gordon Wilson C. Lawrence Zitnick Zachary Ulissi
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.