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

ABSTRACT: 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. Th

SUBMITTER: Kurban H 

PROVIDER: S-EPMC12368103 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature