GEOMIND: A hybrid generative artificial intelligence for geopolymer design and optimization

Sébastien Rousseau; Assil Bouzid; Sylvie Rossignol; Ameni Gharzouni

doi:10.1039/d5dd00383k

January 1, 2026Digital DiscoveryOpen Access

GEOMIND: A hybrid generative artificial intelligence for geopolymer design and optimization

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Key Points

This research aims to enhance the formulation and optimization of geopolymers using a hybrid generative AI approach.
Developed GEOMIND, a hybrid AI system for geopolymer design.
Implemented various algorithms for material formulation optimization.
Conducted extensive simulations to assess formulation accuracy.
Successfully identified optimal formulations for various applications.
GEOMIND outperformed traditional methods in speed and accuracy of formulation predictions.
Achieved significant improvements in material properties as indicated by performance metrics.

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Implication

Evaluates effectiveness of hybrid AI in optimizing geopolymer formulations, suggesting eco-friendly material advancements.

Abstract

Geopolymers are an emerging class of eco-friendly materials with a wide range of applications. Nevertheless, achieving compounds for a specific application requires extensive experimental efforts on finding the accurate formulation...

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Cite This Study

Rousseau et al. (2026) studied this question.

synapsesocial.com/papers/6a250c7d7def13d035e1cb09 https://doi.org/10.1039/d5dd00383k

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GEOMIND: A hybrid generative artificial intelligence for geopolymer design and optimization

Key Points

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Abstract

Discussion

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GEOMIND: A hybrid generative artificial intelligence for geopolymer design and optimization

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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