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<p>This study presents an integrated approach combining Density Functional based Tight Binding (DFTB) calculations with machine learning (ML) techniques to predict the density of states (DOS) in pristine and Zn-doped MgO nanoparticles&#xa0;(NPs). A range of over 60 ML models, including linear models, tree-based ensembles, and neural networks, were evaluated for predictive performance. Among these, the weighted k-nearest neighbor (wkNN) algorithm, particularly when using triweight and biweight kernels, consistently outperformed others, achieving a median RMSE of 0.241 for pristine MgO and 0.386 for Zn-doped samples. The models demonstrated robust performance across various doping concentrations (5–25%) and NP sizes (0.8&#xa0;nm and 0.9&#xa0;nm), with minimal impact of doping levels on prediction accuracy. This integration of DFTB with ML offers a powerful and efficient framework for accelerating electronic property predictions in materials science, supporting the rapid design of advanced materials for applications in electronics, catalysis, and energy storage. The code and data are publicly available at: <a href="https://github.com/KurbanIntelligenceLab/DOS-Nanoparticles-Weighted-kNN">https://github.com/KurbanIntelligenceLab/DOS-Nanoparticles-Weighted-kNN</a>.</p>

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Accelerating density of states prediction in Zn-doped MgO nanoparticles via kernel-optimized weighted k-NN

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Hasan Kurban,
  • Parichit Sharma,
  • Mehmet M. Dalkilic,
  • Mustafa Kurban

摘要

This study presents an integrated approach combining Density Functional based Tight Binding (DFTB) calculations with machine learning (ML) techniques to predict the density of states (DOS) in pristine and Zn-doped MgO nanoparticles (NPs). A range of over 60 ML models, including linear models, tree-based ensembles, and neural networks, were evaluated for predictive performance. Among these, the weighted k-nearest neighbor (wkNN) algorithm, particularly when using triweight and biweight kernels, consistently outperformed others, achieving a median RMSE of 0.241 for pristine MgO and 0.386 for Zn-doped samples. The models demonstrated robust performance across various doping concentrations (5–25%) and NP sizes (0.8 nm and 0.9 nm), with minimal impact of doping levels on prediction accuracy. This integration of DFTB with ML offers a powerful and efficient framework for accelerating electronic property predictions in materials science, supporting the rapid design of advanced materials for applications in electronics, catalysis, and energy storage. The code and data are publicly available at: https://github.com/KurbanIntelligenceLab/DOS-Nanoparticles-Weighted-kNN.