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Peer-reviewedCC BY-NC 4.0Digital Discovery

GEOMIND: a hybrid generative artificial intelligence model for geopolymer design and optimization

  • Sébastien Rousseau
  • Assil Bouzid
  • Sylvie Rossignol
  • Ameni Gharzouni
Open at sourcePDFDOICite

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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Cite

BibTeXRIS
BibTeX
@article{rousseau2026geomind,
  title = {{GEOMIND: a hybrid generative artificial intelligence model for geopolymer design and optimization}},
  author = {Sébastien Rousseau and Assil Bouzid and Sylvie Rossignol and Ameni Gharzouni},
  journal = {Digital Discovery},
  year = {2026},
  month = jun,
  doi = {10.1039/d5dd00383k},
  url = {https://unilim.hal.science/hal-05795593v1},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - GEOMIND: a hybrid generative artificial intelligence model for geopolymer design and optimization
AU  - Sébastien Rousseau
AU  - Assil Bouzid
AU  - Sylvie Rossignol
AU  - Ameni Gharzouni
PY  - 2026
DA  - 2026/06/05
JO  - Digital Discovery
DO  - 10.1039/d5dd00383k
UR  - https://unilim.hal.science/hal-05795593v1
AB  - GEOMIND is a hybrid machine learning model trained on an in-house database to recommend optimal geopolymer formulations for user-defined target properties.
SN  - 2635-098X
ER  -