Towards automatized high-throughput studies of structure-property correlations in gradient two-component organic thin films
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We wish to pursue our machine learning (ML) assisted high-throughput film growth studies, demonstrated in a pioneering work done at the ID10 beamline, on two- and multi-component materials. Our sample preparation chamber allows the deposition of multicomponent organic thin films with lateral gradient distribution of the components. Spatially-resolved X-ray scattering measurements along the gradient axis of the films will provide compositionally-resolved information about the crystalline structure, degree of intermixing, and morphology of the blends. These results will be correlated with the optical properties of the same samples measured prior to the X-ray experiment. This study will not only contribute to a better understanding of structure-property correlations in organic blends but also pave the way to closed-loop experiments with complex materials.



