MSP-LLM：一种用于完整材料合成规划的统一大语言模型框架
MSP-LLM: A Structured Large Language Model Framework for Inorganic Material Synthesis Planning
AI总结：
MSP-LLM提出一种统一的大语言模型框架，通过整合前驱材料预测和合成操作预测，有效解决材料合成规划问题。
摘要：
材料合成规划（MSP）仍然是AI驱动的材料发现中的基础且未被充分探索的瓶颈，因为它不仅需要识别合适的前驱材料，还需要设计连贯的合成操作序列以实现目标材料。尽管已有几种基于AI的方法解决了MSP的孤立子任务，但尚未建立解决整个MSP任务的统一方法。我们提出了MSP-LLM，一种基于大语言模型的统一框架，将MSP建模为由两个组成部分子问题组成的结构化过程：前驱材料预测（PP）和合成操作预测（SOP）。我们的方法引入了一种离散的材料类别作为中间决策变量，将两个任务整合到一个化学上一致的决策链中。对于SOP，我们进一步将分层的前驱类型作为合成相关的归纳偏置，并采用显式条件策略，在自回归解码状态中保留与前驱材料相关的信息。广泛的实验表明，MSP-LLM在PP和SOP以及完整的MSP任务上都优于现有方法，证明了MSP的有效且可扩展的框架，能够加速现实中的材料发现。
英文摘要：
Material synthesis planning (MSP) remains a fundamental and underexplored bottleneck in AI-driven materials discovery, as it requires not only identifying suitable precursor materials but also designing coherent sequences of synthesis operations to realize a target material. Although several AI-based approaches have been proposed to address isolated subtasks of MSP, a methodology for solving the entire MSP task has yet to be established. We propose MSP-LLM, a structured LLM-based framework that formulates MSP as a two-stage process composed of two constituent subproblems: precursor prediction (PP) and synthesis operation prediction (SOP). Our approach introduces a discrete material class as an intermediate decision variable that organizes both tasks into a chemically consistent decision chain. For SOP, we further incorporate hierarchical precursor types as synthesis-relevant inductive biases and employ precursor constraint factorization that preserves precursor-related information in the autoregressive decoding state. Extensive experiments show that MSP-LLM consistently outperforms existing methods on both PP and SOP, as well as on the MSP task, demonstrating an effective and scalable framework with practical potential for autonomous MSP.
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