Atom Classification with Machine Learning and Correlations among Physical Properties of ZnO Nanoparticle

Abstract Machine learning (ML) has been recently used to make sense of large volume of data as data-driven methods to identify correlations and then examine material properties in detail. Herein, we analyze the correlations between structural and electronic properties of ZnO nanoparticles (NPs) obtained from density-functional tight-binding method using Data Science techniques. More clearly, the Pearson correlation coefficients were first computed to perform the relationship among the physical properties of ZnO NPs. Second, we classified Zn and O atoms using optimized geometries of ZnO NPs at different temperatures using various of ML algorithms. Our results show that segregation phenomena and bonding of Zn-O and O-O two-body interactions have a stronger relationship with the orbital energies than that of Zn-Zn. We also observe that a specific type of ML algorithm, tree-based models, performs much better than other types. Additionally, Random Forest outperforms other algorithms and is able to learn ZnO NPs close to perfect.