用于端到端无机晶体合成规划的可解释物理信息增强检索生成语言模型
Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning
AI总结：
本研究开发可解释PIRAG-LM模型，构建含13820种晶体的SSKB，合成方法预测准确率达91.4%，指导合成5种新化合物，为材料发现与实验搭建桥梁。
AI中文摘要：
无机材料的合成规划需要通过将微观热力学稳定性与宏观合成方法、前驱体及加工条件关联起来，同时预测材料的可合成性与可行路线。本研究开发了一种可解释的物理信息增强检索生成语言模型（Physics-Informed Retrieval-Augmented Generation Language Model, PIRAG-LM），用于端到端无机晶体合成规划。我们构建了以材料为中心的结构化合成知识库（Structured Synthesis Knowledge Base, SSKB），其中包含13820种实验合成的无机晶体的路线级记录。PIRAG-LM利用化学、结构和热力学相似性检索历史先例，随后采用结构化大语言模型推理模块提出合成路线、前驱体及加工条件，并评估热力学可行性、动力学及可及性。该模型在合成方法预测中达到91.4%的准确率，而单独使用大语言模型的准确率为72.1%，且能泛化到知识截止后报道的材料。由于该框架依赖检索而非参数化记忆，因此可通过扩展SSKB来提升性能，无需重新训练语言模型。在PIRAG-LM的指导下，我们通过固相法和溶液路线实验合成了五种新化合物：BaMo0.3In0.7O2.95、BaNb0.4In0.6O2.9、Hg[B(CN)4]2、CoCo(CN)6及SrNb2Fe2(PO4)6。这些结果表明，该可解释机器学习方法有助于弥合计算材料发现与实验实现之间的鸿沟。
英文摘要：
Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis methods, precursors, and processing conditions. Here, we develop an interpretable Physics-Informed Retrieval-Augmented Generation Language Model (PIRAG-LM) for end-to-end inorganic crystal synthesis planning. We construct a material-centered Structured Synthesis Knowledge Base (SSKB) containing route-level records for 13,820 experimentally synthesized inorganic crystals. PIRAG-LM retrieves historical precedents using chemical, structural, and thermodynamic similarity, then employs a structured LLM reasoning module to propose routes, precursors, and processing conditions and assess thermodynamic feasibility, kinetics, and accessibility. It achieves 91.4% accuracy in synthesis-method prediction, compared with 72.1% for the LLM alone, and generalizes to materials reported after the knowledge cutoff. Because the framework relies on retrieval rather than parametric memorization, its performance can be improved by expanding the SSKB without retraining the language model. Guided by PIRAG-LM, we experimentally synthesize five new compounds: BaMo0.3In0.7O2.95, BaNb0.4In0.6O2.9, Hg[B(CN)4]2, CoCo(CN)6, and SrNb2Fe2(PO4)6, via solid-state and solution routes. These results demonstrate an interpretable machine-learning approach that helps bridge computational materials discovery and experimental realization.
