用大模型预测量子材料合成路径，准确率超90%
Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
- 基于大模型构建三模块框架，逆向推导反应物与化学方程式
- 准确率从不足40%提升至约90%，支持多步合成流程
- 对不同量子性材料表现稳定，适合材料研发人员快速探索
无机晶体材料的合成对现代技术至关重要，尤其在量子材料开发中。然而，由于实验条件苛刻且需大量试错，设计高效合成流程仍具挑战。本文提出一种基于大语言模型（LLM）的框架，用于预测无机材料（包括量子材料）的合成路径。该框架包含三个模型：LHS2RHS（由反应物预测产物）、RHS2LHS（由产物反推反应物）和TGT2CEQ（为目标化合物生成完整化学方程式）。模型在文本挖掘的合成数据库上微调后，准确率从预训练模型的不足40%提升至常规微调下的约80%，进一步采用广义Tanimoto相似性方法后达到约90%，且对额外合成步骤保持鲁棒性。模型在不同量子性水平的材料上表现相当，表明大模型可有效用于量子材料的平衡化学方程预测。
原文摘要 · Abstract (English)
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.
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