{
  "abstract": "GEOMIND is a hybrid machine learning model trained on an in-house database to recommend optimal geopolymer formulations for user-defined target properties.",
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  "authors": [
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      "name": "Sébastien Rousseau"
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    {
      "name": "Assil Bouzid"
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    {
      "name": "Sylvie Rossignol"
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    {
      "name": "Ameni Gharzouni"
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  "bibtex": "https://research.draconitic.com/paper/dnx5tjvq03cg3.bib",
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  "doi": "10.1039/d5dd00383k",
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  "licence_label": "CC BY-NC 4.0",
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  "pdf_url": "https://unilim.hal.science/hal-05795593/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-05",
  "publisher": "Royal Society of Chemistry",
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  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
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  "title": "GEOMIND: a hybrid generative artificial intelligence model for geopolymer design and optimization",
  "title_text": "GEOMIND: a hybrid generative artificial intelligence model for geopolymer design and optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:58Z",
  "url": "https://research.draconitic.com/paper/dnx5tjvq03cg3",
  "venue": "Digital Discovery",
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