The synthesis of inorganic crystalline materials is essential for modern technology, especially in quantum materials development. However, designing efficient synthesis workflows remains a significant challenge due to the precise experimental conditions and extensive trial and error. Here, we present a framework using large language models (LLMs) to predict synthesis pathways for inorganic materials, including quantum materials. Our framework contains three models: LHS2RHS, predicting products from reactants; RHS2LHS, predicting reactants from products; and TGT2CEQ, generating full chemical equations for target compounds. Fine-tuned on a text-mined synthesis database, our model raises accuracy from under 40% with pretrained models, to under 80% using conventional fine-tuning, and further to around 90% with our proposed generalized Tanimoto similarity, while maintaining robust to additional synthesis steps. Our model further demonstrates comparable performance across materials with varying degrees of quantumness quantified using quantum weight, indicating that LLMs offer a powerful tool to predict balanced chemical equations for quantum materials discovery.

翻译：无机晶体材料的合成对于现代技术至关重要，尤其在量子材料开发领域。然而，由于需要精确的实验条件和大量的试错，设计高效的合成工作流程仍然是一项重大挑战。本文提出一个利用大语言模型预测无机材料（包括量子材料）合成路径的框架。我们的框架包含三个模型：LHS2RHS，从反应物预测产物；RHS2LHS，从产物预测反应物；以及TGT2CEQ，为目标化合物生成完整的化学方程式。通过在文本挖掘的合成数据库上进行微调，我们的模型将准确率从预训练模型的不足40%，提升至使用传统微调方法的不足80%，并进一步通过我们提出的广义Tanimoto相似度提升至约90%，同时对于额外的合成步骤保持稳健性。我们的模型进一步在通过量子权重量化的、具有不同"量子性"程度的材料上表现出可比性能，这表明大语言模型为预测量子材料发现所需的平衡化学方程式提供了一个强大工具。