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  3. Language Models Enable Data-augmented Synthesis Planning For Inorganic Materials.
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  3. Language Models Enable Data-augmented Synthesis Planning For Inorganic Materials.

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Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials.

Thorben Prein1,2,3, Elton Pan4, Janik Jehkul5

  • 1School of Natural Sciences, Technische Universität München, Garching bei München 85748, Germany.

ACS Applied Materials & Interfaces
|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LMs) can predict inorganic synthesis conditions from scientific literature without fine-tuning. Combining LMs with a transformer model (SyntMTE) enables scalable and data-efficient synthesis planning.

Keywords:
large language modelsprecursor recommendationsolid-state synthesissynthesis condition predictionsynthetic data augmentation

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Inorganic synthesis planning traditionally uses heuristics or limited machine learning (ML) models, hindering broad applicability.
  • Existing methods struggle with the vastness and complexity of chemical synthesis data.

Purpose of the Study:

  • To evaluate the capability of off-the-shelf language models (LMs) for recalling and predicting inorganic synthesis conditions.
  • To investigate the potential of LM-generated data for training more effective synthesis planning models.
  • To develop a hybrid workflow for scalable and data-efficient inorganic synthesis planning.

Main Methods:

  • Utilized pre-trained language models (GPT-4.1, Gemini 2.0 Flash, Llama 4 Maverick) for precursor and condition prediction.
  • Ensembled multiple LMs to improve accuracy and reduce computational cost.
  • Trained a transformer model (SyntMTE) on LM-generated reaction recipes and literature data.
  • Evaluated model performance on a held-out dataset and a case study involving solid-state electrolytes (Li7La3Zr2O12).

    • Main Results:

    • Off-the-shelf LMs achieved Top-1 precursor prediction accuracy of up to 53.8% and Top-5 of 66.8%.
    • LMs predicted calcination and sintering temperatures with mean absolute errors <126 °C, outperforming specialized models.
    • Ensembling LMs improved accuracy and reduced inference costs by up to 70%.
    • A model trained solely on LM-generated data showed competitive performance, only 6% worse than literature-trained models.
    • A hybrid model trained on both data types improved performance by up to 4% and reproduced experimental trends for solid-state electrolytes.

    Conclusions:

    • Language models possess significant, untapped potential for inorganic synthesis planning without task-specific fine-tuning.
    • LM-generated data can augment limited literature datasets, leading to more robust and data-efficient training of synthesis prediction models.
    • A hybrid approach combining LMs and transformer models offers a scalable and effective strategy for advancing inorganic synthesis planning.