26 citations · 40 across the 8 of their papers we have counts for
9 papers
Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation
Vladimir Starostin, Maximilian Dax, Alexander Gerlach +3
Reconstructing the structure of thin films and multilayers from measurements of scattered X-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding…
On the analysis of two-time correlation functions: equilibrium vs non-equilibrium systems
Anastasia Ragulskaya, Vladimir Starostin, Fajun Zhang +2
X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of time and length scales. The two-time correlation functio…
Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem
Valentin Munteanu, Vladimir Starostin, Alessandro Greco +5
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, a…
Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
Linus Pithan, Vladimir Starostin, David Mareček +10
Recently, there has been significant interest in applying machine learning (ML) techniques to X-ray scattering experiments, which proves to be a valuable tool for enhancing researc…
Advice on describing Bayesian analysis of neutron and X-ray reflectometry
Andrew R. McCluskey, Andrew J. Caruana, Christy J. Kinane +9
Driven by the availability of modern software and hardware, Bayesian analysis is becoming more popular in neutron and X-ray reflectometry analysis. The understandability and replic…
Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering
Alessandro Greco, Vladimir Starostin, Evelyn Edel +8
This work demonstrates the Python package mlreflect which implements an optimized pipeline for the automized analysis of reflectometry data using machine learning. The package comb…
Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data
Vladimir Starostin, Valentin Munteanu, Alessandro Greco +6
Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells. In situ real-time grazing-incidence X-ray diffraction (G…
Automated matching of two-time X-ray photon correlation maps from protein dynamics with Cahn-Hilliard type simulations using autoencoder networks
S. Timmermann, V. Starostin, A. Girelli +10
We use machine learning methods for an automated classification of experimental XPCS two-time correlation functions from an arrested liquid-liquid phase separation of a protein sol…
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks
Alessandro Greco, Vladimir Starostin, Christos Karapanagiotis +6
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. In this study, we show how a simple a…