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Found 7 papers, 1 papers with code
Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem
Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem.
Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback.
Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data
Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells.
The onset of molecule-spanning dynamics in a multi-domain protein
Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes.
Non-equilibrium Roughness Evolution of Small Molecule Mixed Films Reflecting Equilibrium Phase Behavior
Understanding non-equilibrium phenomena, such as growth, and connecting them to equilibrium phase behavior is a major challenge, in particular for complex multicomponent materials.
Applied Physics Computational Physics
Lattice gas study of thin film growth scenarios and transitions between them: Role of substrate
Thin film growth is investigated in two types of lattice gas models where substrate and film particles are different, expressed by unequal interaction energy parameters.
Soft Condensed Matter
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films.
