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Robertson H, Gresham IJ, Nelson ARJ, Prescott SW, Webber GB, Wanless EJ. Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques. Adv Colloid Interface Sci 2024; 331:103238. [PMID: 38917595 DOI: 10.1016/j.cis.2024.103238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 06/07/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024]
Abstract
Diffuse soft matter interfaces take many forms, from end-tethered polymer brushes or adsorbed surfactants to self-assembled layers of lipids. These interfaces play crucial roles across a multitude of fields, including materials science, biophysics, and nanotechnology. Understanding the nanostructure and properties of these interfaces is fundamental for optimising their performance and designing novel functional materials. In recent years, reflectometry techniques, in particular neutron reflectometry, have emerged as powerful tools for elucidating the intricate nanostructure of soft matter interfaces with remarkable precision and depth. This review provides an overview of selected recent developments in reflectometry and their applications for illuminating the nanostructure of diffuse interfaces. We explore various principles and methods of neutron and X-ray reflectometry, as well as ellipsometry, and discuss advances in their experimental setups and data analysis approaches. Improvements to experimental neutron reflectometry methods have enabled greater time resolution in kinetic measurements and elucidation of diffuse structure under shear or confinement, while innovation in analysis protocols has significantly reduced data processing times, facilitated co-refinement of reflectometry data from multiple instruments and provided greater-than-ever confidence in proposed structural models. Furthermore, we highlight some significant research findings enabled by these techniques, revealing the organisation, dynamics, and interfacial phenomena at the nanoscale. We also discuss future directions and potential advancements in reflectometry techniques. By shedding light on the nanostructure of diffuse interfaces, reflectometry techniques enable the rational design and tailoring of interfaces with enhanced properties and functionalities.
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Affiliation(s)
- Hayden Robertson
- College of Science, Engineering and Environment, University of Newcastle, Callaghan, NSW 2308, Australia; Soft Matter at Interfaces, Technical University of Darmstadt, Darmstadt D-64289, Germany
| | - Isaac J Gresham
- School of Chemistry, University of Sydney, Sydney, NSW 2006, Australia
| | - Andrew R J Nelson
- Australian Centre for Neutron Scattering, ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia
| | - Stuart W Prescott
- School of Chemical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | - Grant B Webber
- College of Science, Engineering and Environment, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Erica J Wanless
- College of Science, Engineering and Environment, University of Newcastle, Callaghan, NSW 2308, Australia.
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2
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Doucet M, Candeago R, Wang H, Browning JF, Su X. Studying Transient Phenomena in Thin Films with Reinforcement Learning. J Phys Chem Lett 2024; 15:4444-4450. [PMID: 38626466 DOI: 10.1021/acs.jpclett.4c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. By mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.
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Affiliation(s)
- Mathieu Doucet
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Riccardo Candeago
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hanyu Wang
- Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - James F Browning
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Xiao Su
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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Munteanu V, Starostin V, Greco A, Pithan L, Gerlach A, Hinderhofer A, Kowarik S, Schreiber F. Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge. J Appl Crystallogr 2024; 57:456-469. [PMID: 38596736 PMCID: PMC11001411 DOI: 10.1107/s1600576724002115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/03/2024] [Indexed: 04/11/2024] Open
Abstract
Due to the ambiguity related 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. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.
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Affiliation(s)
- Valentin Munteanu
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Vladimir Starostin
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Alessandro Greco
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Linus Pithan
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Alexander Gerlach
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | | | - Stefan Kowarik
- Department of Physical Chemistry, University of Graz, Heinrichstraße 28, 8010 Graz, Austria
| | - Frank Schreiber
- University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
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Pithan L, Starostin V, Mareček D, Petersdorf L, Völter C, Munteanu V, Jankowski M, Konovalov O, Gerlach A, Hinderhofer A, Murphy B, Kowarik S, Schreiber F. Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:1064-1075. [PMID: 37850560 PMCID: PMC10624034 DOI: 10.1107/s160057752300749x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/26/2023] [Indexed: 10/19/2023]
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 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.
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Affiliation(s)
- Linus Pithan
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Vladimir Starostin
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - David Mareček
- Physikalische und Theoretische Chemie, Universität Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Lukas Petersdorf
- Institut für Experimentelle und Angewandte Physik, Universität Kiel, Leibnizstrasse 19, 24118 Kiel, Germany
| | - Constantin Völter
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Valentin Munteanu
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Maciej Jankowski
- ESRF – The European Synchrotron, 71 Avenue des Martyrs, CS 40220, 38043 Grenoble Cedex 9, France
| | - Oleg Konovalov
- ESRF – The European Synchrotron, 71 Avenue des Martyrs, CS 40220, 38043 Grenoble Cedex 9, France
| | - Alexander Gerlach
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Alexander Hinderhofer
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Bridget Murphy
- Institut für Experimentelle und Angewandte Physik, Universität Kiel, Leibnizstrasse 19, 24118 Kiel, Germany
| | - Stefan Kowarik
- Physikalische und Theoretische Chemie, Universität Graz, Heinrichstrasse 28, 8010 Graz, Austria
| | - Frank Schreiber
- Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
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Hinderhofer A, Greco A, Starostin V, Munteanu V, Pithan L, Gerlach A, Schreiber F. Machine learning for scattering data: strategies, perspectives and applications to surface scattering. J Appl Crystallogr 2023; 56:3-11. [PMID: 36777139 PMCID: PMC9901926 DOI: 10.1107/s1600576722011566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/30/2022] [Indexed: 01/25/2023] Open
Abstract
Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.
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Affiliation(s)
- Alexander Hinderhofer
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany,Correspondence e-mail:
| | - Alessandro Greco
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Vladimir Starostin
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Valentin Munteanu
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Linus Pithan
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Alexander Gerlach
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
| | - Frank Schreiber
- Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany
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Scatigno C, Festa G. Neutron Imaging and Learning Algorithms: New Perspectives in Cultural Heritage Applications. J Imaging 2022; 8:jimaging8100284. [PMID: 36286378 PMCID: PMC9605401 DOI: 10.3390/jimaging8100284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/04/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Recently, learning algorithms such as Convolutional Neural Networks have been successfully applied in different stages of data processing from the acquisition to the data analysis in the imaging context. The aim of these algorithms is the dimensionality of data reduction and the computational effort, to find benchmarks and extract features, to improve the resolution, and reproducibility performances of the imaging data. Currently, no Neutron Imaging combined with learning algorithms was applied on cultural heritage domain, but future applications could help to solve challenges of this research field. Here, a review of pioneering works to exploit the use of Machine Learning and Deep Learning models applied to X-ray imaging and Neutron Imaging data processing is reported, spanning from biomedicine, microbiology, and materials science to give new perspectives on future cultural heritage applications.
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Mareček D, Oberreiter J, Nelson A, Kowarik S. Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement. J Appl Crystallogr 2022; 55:1305-1313. [PMID: 36249496 PMCID: PMC9533750 DOI: 10.1107/s2053273322008051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/11/2022] [Indexed: 11/10/2022] Open
Abstract
An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector q, as is commonly done, but as a function of both q and time. The real-space structures extracted from the XRR curves are restricted to be solutions of a physics-informed growth model and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves R(q, t) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves, even if they are sparsely sampled, with a sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times. The approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with less beam damage.
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Affiliation(s)
- David Mareček
- Physikalische und Theoretische Chemie, Universität Graz, Heinrichstraße 28, Graz, 8010, Austria
| | - Julian Oberreiter
- Physikalische und Theoretische Chemie, Universität Graz, Heinrichstraße 28, Graz, 8010, Austria
| | - Andrew Nelson
- ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia
| | - Stefan Kowarik
- Physikalische und Theoretische Chemie, Universität Graz, Heinrichstraße 28, Graz, 8010, Austria,Correspondence e-mail:
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8
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Mareček D, Oberreiter J, Nelson A, Kowarik S. Faster and lower-dose X-ray reflectivity measurements enabled by physics-informed modeling and artificial intelligence co-refinement. J Appl Crystallogr 2022. [DOI: 10.1107/s1600576722008056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
An approach is presented for analysis of real-time X-ray reflectivity (XRR) process data not just as a function of the magnitude of the reciprocal-space vector q, as is commonly done, but as a function of both q and time. The real-space structures extracted from the XRR curves are restricted to be solutions of a physics-informed growth model and use state-of-the-art convolutional neural networks (CNNs) and differential evolution fitting to co-refine multiple time-dependent XRR curves R(q, t) of a thin film growth experiment. Thereby it becomes possible to correctly analyze XRR data with a fidelity corresponding to standard fits of individual XRR curves, even if they are sparsely sampled, with a sevenfold reduction of XRR data points, or if the data are noisy due to a 200-fold reduction in counting times. The approach of using a CNN analysis and of including prior information through a kinetic model is not limited to growth studies but can be easily extended to other kinetic X-ray or neutron reflectivity data to enable faster measurements with less beam damage.
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