Python Software for Multimodal Optimization of X-ray Reflectivity Data using First Principles Theory

Diffraction-based experimental techniques like X-ray Reflectivity (XRR) determine the distribution of electrons at the surface of a crystalline solid but inverting this data to obtain the atomic structure of the surface is a challenge. To overcome this obstacle, we develop Python software which optimizes the surface structure by utilizing energetic information from DFT alongside data from multiple experimental measurements under different conditions. Our work leverages Python object orientation and scientific libraries to create modular and flexible software with access to powerful optimization techniques. Using the SrTiO3 (001) surface as an example, we determine interfacial structure using known low energy surface terminations from DFT and experimental measurements from X-rays which are resonant and non-resonant with the Sr K-edge. Using this joint experimental-theoretical approach to investigate the interfacial structure-property relationship provides insights which could increase the performance of diverse technologies such as energy storage and conversion and semiconductor fabrication.