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<p>Synthesis pathway planning is a critical step in the design and discovery of novel inorganic compounds with desirable properties, but existing methods are often constrained by limited generalizability. This study develops a specialized language model, SynPathLM, based on tens of thousands of inorganic synthesis reactions mined from scientific literature using natural language processing (NLP) techniques, which are converted into a structured language. The model achieves end-to-end retrosynthetic planning, enabling precursor recommendation for target compounds, prediction of reaction types, and forecasting of key step temperatures (e.g., calcination and sintering). To address the inherent limitations of large language models (LLMs) in numerical prediction, we further propose SynPathLM-Reg, which integrates MAPP physicochemical prior fusion, attention pooling, a residual regression head, and temperature distribution oversampling design on top of the language model encoder. This significantly improves temperature regression accuracy, reducing the mean absolute error (MAE) for calcination temperature prediction by 54.0% and for sintering temperature prediction by 47.1%. This provides a new solution for efficient and comprehensive synthesis path planning of inorganic materials.</p>

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Predicting Inorganic Material Synthesis Pathways Based on Language Models

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Keyu Ji,
  • Kuanping Gong,
  • Yongquan Jiang,
  • Yan Yang,
  • Zigang Deng

摘要

Synthesis pathway planning is a critical step in the design and discovery of novel inorganic compounds with desirable properties, but existing methods are often constrained by limited generalizability. This study develops a specialized language model, SynPathLM, based on tens of thousands of inorganic synthesis reactions mined from scientific literature using natural language processing (NLP) techniques, which are converted into a structured language. The model achieves end-to-end retrosynthetic planning, enabling precursor recommendation for target compounds, prediction of reaction types, and forecasting of key step temperatures (e.g., calcination and sintering). To address the inherent limitations of large language models (LLMs) in numerical prediction, we further propose SynPathLM-Reg, which integrates MAPP physicochemical prior fusion, attention pooling, a residual regression head, and temperature distribution oversampling design on top of the language model encoder. This significantly improves temperature regression accuracy, reducing the mean absolute error (MAE) for calcination temperature prediction by 54.0% and for sintering temperature prediction by 47.1%. This provides a new solution for efficient and comprehensive synthesis path planning of inorganic materials.