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<p>This study explores the effect of zinc oxide (ZnO) nanomaterial doping on the electro-optical properties of 5CB-coded nematic liquid crystals and predicts these properties using machine learning algorithms. We produced seven composite structures with varying ZnO doping ratios and measured their electro-optical transmittance. Furthermore, a prediction model using four different machine learning algorithms (k-Nearest Neighbors, Decision Tree, Random Forest, and Extra Trees) was developed, which predicts optical transmittance as a function of voltage and doping ratio. The Extra Trees algorithm demonstrated the best prediction accuracy, achieving an <i>R</i><sup>2</sup> value of 91% on the experimental dataset. Subsequently, a new composite with a different doping ratio was then experimentally prepared and measured to validate the model, which was trained on the experimental dataset. This study highlights the utility of machine learning for predicting the electro-optical characteristics of doped liquid crystal structures, resulting in considerable time and resource savings in experimental procedures.</p>

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Machine learning predictions of electro-optical properties in ZnO-doped nematic liquid crystals

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Mustafa Aksoy,
  • Yesim Aygul,
  • Onur Ugurlu,
  • Umit Huseyin Kaynar,
  • Gulnur Onsal

摘要

This study explores the effect of zinc oxide (ZnO) nanomaterial doping on the electro-optical properties of 5CB-coded nematic liquid crystals and predicts these properties using machine learning algorithms. We produced seven composite structures with varying ZnO doping ratios and measured their electro-optical transmittance. Furthermore, a prediction model using four different machine learning algorithms (k-Nearest Neighbors, Decision Tree, Random Forest, and Extra Trees) was developed, which predicts optical transmittance as a function of voltage and doping ratio. The Extra Trees algorithm demonstrated the best prediction accuracy, achieving an R2 value of 91% on the experimental dataset. Subsequently, a new composite with a different doping ratio was then experimentally prepared and measured to validate the model, which was trained on the experimental dataset. This study highlights the utility of machine learning for predicting the electro-optical characteristics of doped liquid crystal structures, resulting in considerable time and resource savings in experimental procedures.