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 unified methodology for solving the entire MSP task has yet to be established. We propose MSP-LLM, a unified LLM-based framework that formulates MSP as a structured 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 OP, we further incorporate hierarchical precursor types as synthesis-relevant inductive biases and employ an explicit conditioning strategy 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 complete MSP task, demonstrating an effective and scalable framework for MSP that can accelerate real-world materials discovery.

翻译：材料合成规划（MSP）仍然是AI驱动材料发现领域一个基础且尚未被充分探索的瓶颈问题，因为它不仅需要识别合适的前驱体材料，还需要设计连贯的合成操作序列以实现目标材料。尽管已有一些基于AI的方法被提出来解决MSP的孤立子任务，但一个用于解决整个MSP任务的统一方法尚未建立。我们提出了MSP-LLM，一个基于LLM的统一框架，它将MSP构建为一个由两个组成部分构成的、结构化的过程：前驱体预测（PP）和合成操作预测（SOP）。我们的方法引入了一个离散的材料类别作为中间决策变量，将这两个任务组织成一个化学上一致的决策链。对于SOP，我们进一步引入了层次化的前驱体类型作为与合成相关的归纳偏置，并采用了一种显式的条件化策略，在自回归解码状态中保留前驱体相关信息。大量实验表明，MSP-LLM在PP、SOP以及完整的MSP任务上均持续优于现有方法，证明其是一个有效且可扩展的MSP框架，能够加速现实世界的材料发现。