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<p>This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), <i>K</i>-nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(96.21\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>96.21</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable&#xa0;for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative&#xa0;substance informatics and industrial quality control.</p>

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High-Precision Classification of Transparent Conductive Oxides with Optical Signatures using XGBoost Algorithm for Accelerated Material Screening

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Norhazwani Md Yunos,
  • Camellia Doroody,
  • Ong Ke Sheng,
  • Hasrul Nisham Rosly,
  • Chenyoushi Xu,
  • Zheng-Jie Feng,
  • Zuraini Othman,
  • Yuan Dong

摘要

This study introduces a machine learning (ML) adaptive interface for classifying transparent conductive oxide (TCO) materials by examining their optical characteristics, such as their wavelength, optical density, absorption, and transmission. Six supervised ML models were considered in this study, namely decision tree, random forest, extreme gradient boosting (XGBoost), support vector classifier (SVC), K-nearest neighbors (KNN), and multilayer perceptron (MLP). These models were evaluated using metrics such as accuracy, precision, recall, F1-score, confusion matrices, receiver operating characteristic (ROC) curves, and learning curves. The comparison findings revealed that XGBoost delivered the best performance, achieving an accuracy of \(96.21\%\) 96.21 % and a perfect area under the curve (AUC) score of 1.00, effectively distinguishing all five sampled TCO classes: aluminum-doped zinc oxide (AZO), fluorine-doped tin oxide (FTO), indium tin oxide (ITO), magnesium-doped zinc oxide (MZO), and zinc oxide (ZnO). These findings reveal ML’s potential for both scientific and industrial integration in material science research. XGBoost, as a high-precision model, may significantly reduce process time and equipment-intensive laboratory characterizations by allowing for the rapid and precise classification of data from optical measurements. This expedites the search for materials suitable for use in photovoltaics, optoelectronics, and display technologies, all of which require high transparency and conductivity. Overall, this work expands the potential for using ML to categorize materials, particularly XGBoost. It provides both scientific understanding and practical applications in innovative substance informatics and industrial quality control.