We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several molecular properties in the QM9 dataset and 28 materials properties. Only composition-based input strings are used as the model input and we fine tune on only the generative loss. We broadly find that LLaMA 3, when fine-tuned using the SMILES representation of molecules, provides useful regression results which can rival standard materials property prediction models like random forest or fully connected neural networks on the QM9 dataset. Not surprisingly, LLaMA 3 errors are 5-10x higher than those of the state-of-the-art models that were trained using far more granular representation of molecules (e.g., atom types and their coordinates) for the same task. Similarly, LLaMA 3 provides comparable, although slightly worse, accuracy relative to random forest and elemental descriptors when using just compound chemical description on our set of 28 materials properties. Interestingly, LLaMA 3 provides improved predictions compared to GPT-3.5 and GPT-4o. This work highlights the versatility of LLMs, suggesting that LLM-like generative models can potentially transcend their traditional applications to tackle complex physical phenomena, thus paving the way for future research and applications in chemistry, materials science and other scientific domains.

翻译：我们证明了大语言模型（LLMs）在材料与分子性质回归任务上的能力，这显著偏离了传统LLM应用场景。我们以Meta AI开发的LLaMA 3模型为基准，对QM9数据集中的若干分子性质及28项材料性质进行测试。仅以基于组分的输入字符串作为模型输入，并仅针对生成损失进行微调。研究发现，当使用SMILES分子表示法进行微调时，LLaMA 3能提供与标准材料性质预测模型（如随机森林或全连接神经网络）在QM9数据集上相媲美的回归结果。不出所料，对于相同任务，LLaMA 3的误差比采用更精细分子表示（如原子类型及其坐标）训练的最先进模型高出5-10倍。类似地，在仅使用化合物化学描述的情况下，LLaMA 3对我们28项材料性质数据集的预测精度与随机森林和元素描述符方法相当（虽略逊一筹）。值得注意的是，LLaMA 3的预测性能优于GPT-3.5和GPT-4o。本研究凸显了LLMs的多功能性，表明类LLM生成模型有望超越其传统应用场景，涉足复杂物理现象的建模，从而为化学、材料科学及其他科学领域的未来研究与应用开辟新路径。