用大模型+物理模拟，自动设计无机材料合成路径。
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
- 结合热力学数据库与简化动力学模型，评估大语言模型的合成规划能力。
- 在铌氧体系中，大模型生成路径比传统算法更可行。
- 适合材料研发、计算化学领域，尤其擅长复杂反应路径设计。
现代生成式机器学习模型可提出具有特定性质的新型无机晶体材料；然而，由于相关物理过程复杂且计算工具有限，这些材料的合成规划仍具挑战。本文提出一种新型混合框架，通过将热力学数据库与简化动力学模型结合，评估大语言模型（LLM）在无机合成规划中的表现。以铌-氧体系为例，该体系包含多个工业相关的氧化物相，数据特征明确。在计算模拟中，我们对比了大模型生成的合成路径与经典路径规划算法的结果，发现大模型隐含先验知识能生成更具可行性策略。在此评估设置中，传统搜索方法主要作为参照而非直接竞争者，凸显该问题的相对复杂性，并表明大模型隐含先验在关键环节的价值。
原文摘要 · Abstract (English)
Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to the complexity of the associated physical processes and limited availability of computational tools. We introduce a novel hybrid framework to evaluate Large Language Models (LLMs) in inorganic synthesis planning by combining thermodynamic databases with simplified kinetics models to approximate realistic synthesis conditions. As a case study, we focus on the niobium-oxygen system, which features multiple industrially relevant oxide phases with well-characterized data. In computational simulations, we compare LLM-generated synthesis routes with classical path-planning algorithms, showing that the implicit priors in LLMs can yield more viable strategies. In our evaluation setting, classical search methods serve primarily as a foil rather than a direct competitor. This illustrates the relative complexity of the problem and highlights where the LLM's implicit priors add value.
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