Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.

ABSTRACT: Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of o

SUBMITTER: Pithan L 

PROVIDER: S-EPMC10624034 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature