[2602.07543] MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning

Computer Science > Artificial Intelligence arXiv:2602.07543 (cs) [Submitted on 7 Feb 2026 (v1), last revised 1 Mar 2026 (this version, v3)] Title:MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning Authors:Heewoong Noh, Gyoung S. Na, Namkyeong Lee, Chanyoung Park View a PDF of the paper titled MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning, by Heewoong Noh and 3 other authors View PDF HTML (experimental) Abstract: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 SOP, we further incorporate hierarchical precursor types as synthesis-relevant inductive biases and employ an explicit conditioning strategy that preserves precursor-related informat...