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

ABSTRACT: Inorganic synthesis planning currently relies primarily on heuristic approaches or machine learning models trained on limited data sets, which constrains its generality. We demonstrate that language models (LMs) without task-specific fine-tuning can recall synthesis conditions reported in the scientific literature. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash, and Llama 4 Maverick achieve a Top-1 precursor prediction accuracy of up to 53.8% and a Top-5 performance of 66.8% on a held-out set of 1000 reactions. They also predict calcination and sintering temperatures with mean absolute errors of <126 °C, matching or surpassing specialized regression models. Ensembling these LMs further enhances predictive accuracy and reduces inference cost per prediction by up to 70%. Given the broad, cross-domain knowledge of LMs, we evaluate whether they enable knowledge transfer by training a transformer, SyntMTE, on 28,548 LM-generated reaction recipes. Compared to a model trained on literature-reported data, we find that a model trained solely on LM-generated data exhibits competitive performance (only 6% worse). Conversely, a model trained on both the LM-generated and literature-reported data improves performance by up to 4%. In a case study on Li₇La₃Zr₂O₁₂ solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable and data-efficient inorganic synthesis planning.

SUBMITTER: Prein T 

PROVIDER: S-EPMC12754754 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature