Abstract
Ferromagnetic perovskite oxides, particularly La(0.7)Sr(0.3)MnO(3) (LSMO), show significant promise for spintronics and electromagnetic applications due to their unique half-metallicity and colossal magnetoresistance properties. These properties are known to arise from Mn-O-Mn double-exchange interactions, which are directly related to microscopic lattice structures. However, since the microscopic structure in LSMO is highly sensitive to various material parameters, such as thickness, lattice strain, oxygen deficiency, and cation stoichiometry, understanding the intricate relationship between the microscopic structures and the resulting physical properties of LSMO remains challenging. Herein, a machine learning approach is introduced to characterize ferromagnetic LSMO thin films by featurization of their surface morphology. Using an ensemble machine learning method, the non-linear correlations between surface morphology and the electronic, magnetic properties of LSMO thin films are captured and modeled. Based on these estimated correlations, LSMO thin films are classified into five representative types, each characterized by distinctive properties and surface morphologies. These results imply that surface morphology can reveal hidden information about the strongly correlated properties of ferromagnetic LSMO thin films. Consequently, the machine learning-based approach provides an efficient method for understanding the correlated material properties of ferromagnetic oxides and related materials through surface morphology analysis.