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

2026-02-07

AI Summary

This work addresses the long-standing challenge in materials synthesis planning (MSP)—the absence of a unified framework capable of simultaneously and accurately predicting both precursor chemicals and the sequence of synthesis operations. To this end, the authors propose the first end-to-end unified approach that decomposes MSP into two subtasks: precursor prediction and operation prediction. They introduce discrete material categories as an intermediate latent variable to construct a chemically consistent decision chain. Furthermore, by incorporating hierarchical precursor types as inductive bias and leveraging structured generation from large language models with an explicit conditional autoregressive decoding strategy, the method enhances both chemical plausibility and model coherence. Experimental results demonstrate that the proposed approach significantly outperforms existing methods across precursor prediction, operation prediction, and the full MSP task, confirming its effectiveness and scalability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 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.

Problem

• Material Synthesis Planning
• Precursor Prediction
• Synthesis Operation Prediction
• AI-driven Materials Discovery
• Large Language Model

Innovation

• Material Synthesis Planning
• Large Language Model
• Precursor Prediction
• Synthesis Operation Prediction
• Inductive Bias