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PreprintCC BYChemRxiv

Text-mined dataset of sol-gel synthesis protocols with phase purity outcomes using large language models

  • Wesley Y. Chen1,2,3,4
  • Sanghoon Lee1,2,3,5
  • Tim Kodalle6,7
  • Philothei Sahinidis1,2,8,5
  • Gerbrand Ceder1,2,3,5
  • Carolin M. Sutter-Fella6,7
  • Anubhav Jain1,2
8 affiliations
  1. 1Lawrence Berkeley National Lab
  2. 2Energy Technologies Area
  3. 3Department of Materials Science and Engineering
  4. 4Northwestern University
  5. 5University of California,
  6. 6Lawrence Berkeley National Laboratory
  7. 7Molecular Foundry
  8. 8Department of Electrical Engineering and Computer
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Abstract

Sol-gel synthesis is a versatile method for producing inorganic materials that offers advantages such as atomic-level mixing and lower processing temperatures over traditional solid-state methods. Despite these benefits, achieving phase purity remains a challenge due to the process's sensitivity to synthesis parameters such as precursor choice, solvent selection, and heating conditions. Negative results, such as phase-impure outcomes, are essential for understanding sol-gel reaction pathways, but are largely absent from existing curated datasets. This work leverages Gemini 3.0 Flash to construct a text-mined inorganic sol-gel database with phase purity information. The dataset contains ~148,000 synthesis records, including ~28,000 phase-impure entries. All extracted entities achieved F1 scores greater than 0.89. At the record level, 68% of extracted records match human annotation on all evaluated fields. Furthermore, to validate the scientific utility of our dataset, we demonstrate that the aggregated data reproduces previously reported phase-purity trends for BiFeO 3 thin films.

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Cite

BibTeXRIS
BibTeX
@misc{chen2026textmined,
  title = {{Text-mined dataset of sol-gel synthesis protocols with phase purity outcomes using large language models}},
  author = {Wesley Y. Chen and Sanghoon Lee and Tim Kodalle and Philothei Sahinidis and Gerbrand Ceder and Carolin M. Sutter-Fella and Anubhav Jain},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15006472/v2},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006472/v2},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Text-mined dataset of sol-gel synthesis protocols with phase purity outcomes using large language models
AU  - Wesley Y. Chen
AU  - Sanghoon Lee
AU  - Tim Kodalle
AU  - Philothei Sahinidis
AU  - Gerbrand Ceder
AU  - Carolin M. Sutter-Fella
AU  - Anubhav Jain
PY  - 2026
DA  - 2026/10/13
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15006472/v2
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006472/v2
AB  - Sol-gel synthesis is a versatile method for producing inorganic materials that offers advantages such as atomic-level mixing and lower processing temperatures over traditional solid-state methods. Despite these benefits, achieving phase purity remains a challenge due to the process's sensitivity to synthesis parameters such as precursor choice, solvent selection, and heating conditions. Negative results, such as phase-impure outcomes, are essential for understanding sol-gel reaction pathways, but are largely absent from existing curated datasets. This work leverages Gemini 3.0 Flash to construct a text-mined inorganic sol-gel database with phase purity information. The dataset contains ~148,000 synthesis records, including ~28,000 phase-impure entries. All extracted entities achieved F1 scores greater than 0.89. At the record level, 68% of extracted records match human annotation on all evaluated fields. Furthermore, to validate the scientific utility of our dataset, we demonstrate that the aggregated data reproduces previously reported phase-purity trends for BiFeO 3 thin films.
ER  -