Amorphous and nanocrystalline thin films feature unique functional properties of interest for future information storage, computing, and detection technologies. To realize the promise of these, however, requires understanding of the fundamental atomic structure origins of these exciting functionalities. To enable both applied and basic understanding of these materials requires advances in materials characterization.
We focus on leveraging machine learning and data science algorithms to separate the low intensity X-ray scattering signal of films from that of their single crystal substrates, such that data quality and fidelity are sufficient for pair distribution function analysis.
We are concurrently developing data processing algorithms and deriving materials understanding for amorphous, nanocrystalline, and disordered thin film materials on single crystal substrates.
