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AI/Machine Learning and Sol-Gel Derived Hybrid Materials: A Winning Coupling.

Aurelio Bifulco¹, Giulio Malucelli^2,3

¹Department of Chemical, Materials and Industrial Production Engineering (DICMaPI), University of Naples Federico II, Piazzale Tecchio 80, 80125 Napoli, Italy.

Molecules (Basel, Switzerland)

|July 30, 2025

Summary

Artificial intelligence (AI) and machine learning (ML) accelerate the characterization of hybrid organic-inorganic materials. These AI/ML strategies leverage existing literature data to predict material properties, reducing experimental time and enhancing reliability.

Keywords:

artificial intelligence decision trees hybrid materials machine learning neural networks sol-gel method

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Area of Science:

Materials Science and Engineering
Polymer Science
Nanotechnology
Sol-Gel Chemistry

Background:

Traditional experimental research for polymeric materials and hybrid organic-inorganic systems relies on extensive testing to establish structure-property-processing correlations.
These necessary tests are often time-consuming, resource-intensive, and require significant effort for reproducibility and reliability.
Design of Experiments (DoEs) has improved efficiency but further advancements are needed for comprehensive material characterization.

Purpose of the Study:

To provide an overview of the current applications of artificial intelligence (AI) and machine learning (ML) in the field of sol-gel-derived hybrid materials.
To highlight how AI/ML strategies can optimize the prediction of material properties and reduce experimental workload.
To explore the potential of AI/ML in accelerating research and development of novel hybrid material systems.

Main Methods:

Utilizing machine learning (ML) strategies to analyze and learn from existing literature data on material systems.
Developing AI models capable of predicting key parameters for new, similar material systems.
Focusing on the application of AI/ML within the specific domain of sol-gel-derived hybrid materials.

Main Results:

AI/ML approaches enable the prediction of material properties using available, even incomplete, literature data.
These strategies significantly minimize experimental errors and maximize the reliability of material characterization.
Demonstration of AI/ML's effectiveness in streamlining the study of sol-gel-derived hybrid materials.

Conclusions:

AI/ML represents a significant advancement beyond traditional methods and DoEs for material characterization.
The integration of AI/ML accelerates the discovery and optimization of sol-gel-derived hybrid materials.
Future research in hybrid materials will increasingly benefit from the predictive power of AI/ML strategies.