Distinguishing Isotropic and Anisotropic Signals for X-ray Total Scattering using Machine Learning

Abstract

Understanding structure-property relationships is essential for advancing technologies based on thin films. X-ray pair distribution function (PDF) analysis can access relevant atomic structure details spanning local-, mid-, and long-range order. While X-ray PDF has been adapted for thin films on amorphous substrates, measurements on single crystal substrates are necessary to accurately determine structure origins for some thin film materials, especially those for which the substrate changes the accessible structure and properties. However, when measuring films on single crystal substrates, high intensity anisotropic Bragg spots saturate 2D detector images, overshadowing the thin films' isotropic scattering signal. This renders previous data processing methods for films on amorphous substrates unsuitable for films on single crystal substrates. To address this measurement need, we developed IsoDAT2D, an innovative data processing approach using unsupervised machine learning algorithms. The program combines non-negative matrix factorization and hierarchical agglomerative clustering to separate thin film and single crystal substrate X-ray scattering signals. We use SimDAT2D, a program we developed to generate synthetic thin film data, to validate IsoDAT2D. We also use IsoDAT2D to isolate X-ray total scattering signal from a thin film on a single crystal substrate. The resulting PDF data are compared to similar data processed using previous methods, demonstrating superior performance relative to substrate subtraction with a single crystal substrate and similar performance to substrate subtraction from an amorphous substrate. With IsoDAT2D, there are new opportunities to expand PDF to a wider variety of thin films, including those on single crystal substrates, with which new structure-property relationships can be elucidated to enable fundamental understanding and technological advances.