Autonomous X-ray Scattering

Recent advances in synchrotron scattering methods for probing the structure and dynamics of colloids

Recent progress in synchrotron based X-ray scattering methods applied to colloid science is reviewed. An important figure of merit of these techniques is that they enable in situ investigations of colloidal systems under the desired thermophysical and rheological conditions. An ensemble averaged simultaneous structural and dynamical information can be derived albeit in reciprocal space. Significant improvements in X-ray source brilliance and advances in detector technology have overcome some of the limitations in the past. Notably coherent X-ray scattering techniques have become more competitive and they provide complementary information to laboratory based real space methods. For a system with sufficient scattering contrast, size ranges from nm to several μm and time scales down to μs are now amenable to X-ray scattering investigations. A wide variety of sample environments can be combined with scattering experiments further enriching the science that could be pursued by means of advanced X-ray scattering instruments. Some of these recent progresses are illustrated via representative examples. To derive quantitative information from the scattering data, rigorous data analysis or modeling is required. Development of powerful computational tools including the use of artificial intelligence have become the emerging trend.

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

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

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 organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.