Summary of Fine-tuned Language Models Generate Stable Inorganic Materials As Text, by Nate Gruver et al.
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
by Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ulissi
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method fine-tunes large language models to generate stable materials. By leveraging text-encoded atomistic data, this approach is simple yet reliable, yielding around 90% of sampled structures that adhere to physical constraints on atom positions and charges. The strongest model, LLaMA-2 70B, generates metastable materials at a rate twice that of CDVAE, a competing diffusion model. This fine-tuning enables unconditional generation of stable material, infilling of partial structures, and text-conditional generation. Surprisingly, the biases in pre-trained language models are well-suited for atomistic data, as shown by the improvement in capturing crystal structure symmetries with increasing model scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to use big computers to help us create new materials that are really good at doing things like being strong or insulating electricity. We take a special kind of language model and teach it about atoms and how they fit together. This helps the computer generate new materials that might be able to do things we need them to do, like make really strong materials or help keep our homes warm in winter. This is an exciting area of research because it could lead to discoveries that can benefit many people. |
Keywords
* Artificial intelligence * Diffusion model * Fine tuning * Language model * Llama
