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

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 model...

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Bibliographic Details
Main Authors: Hasan Kurban, Parichit Sharma, Mehmet M. Dalkilic, Mustafa Kurban
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
DFTB
ML
DOS
Zn-doped MgO nanoparticles
WkNN
Online Access:https://doi.org/10.1038/s41598-025-07887-6
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https://doi.org/10.1038/s41598-025-07887-6

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