Kurban Hasan, Sharma Parichit, Dalkilic Mehmet M, Kurban Mustafa
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Computer Science Department, Indiana University, Bloomington, IN, 47405, USA.
Sci Rep. 2025 Aug 20;15(1):30524. doi: 10.1038/s41598-025-07887-6.
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.
The online version contains supplementary material available at 10.1038/s41598-025-07887-6.
本研究提出了一种将基于密度泛函的紧束缚（DFTB）计算与机器学习（ML）技术相结合的综合方法，以预测原始和锌掺杂的氧化镁纳米颗粒（NPs）的态密度（DOS）。评估了包括线性模型、基于树的集成模型和神经网络在内的60多种ML模型的预测性能。其中，加权k近邻（wkNN）算法，特别是在使用三权和双权核时，始终优于其他算法，原始氧化镁的RMSE中位数为0.241，锌掺杂样品的RMSE中位数为0.386。这些模型在各种掺杂浓度（5%-25%）和NP尺寸（0.8纳米和0.9纳米）下都表现出稳健的性能，掺杂水平对预测准确性的影响最小。DFTB与ML的这种整合为加速材料科学中的电子性质预测提供了一个强大而有效的框架，支持快速设计用于电子、催化和储能应用的先进材料。代码和数据可在以下网址公开获取：https://github.com/KurbanIntelligenceLab/DOS-Nanoparticles-Weighted-kNN。
在线版本包含可在10.1038/s41598-025-07887-6获取的补充材料。
