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Time is the most valuable parameter in synchrotron experiments. This is costly and some of the experiments suffer from low efficiency due to low counting statistics. With today's high processing power long experiments are run in a shorter time and increase efficiency. With optimization algorithms time in "counting-hungry" experiments reduced by factor of 10. Our project is to develop a new method to analyze the chemical properties of complex materials non-destructively and efficiently, such as high entropy materials subjected to corrosion processes. A better understanding of the corrosion process will help to develop corrosion-resistant materials and reduce the cost of corrosion damage, which averages around 2.5 trillion USD annually.
The components that are used in structural and in high temperature applications generally face significant challenges with respect to oxidation behaviours and metalworking processes. In most of the cases, harsh environmental conditions lead materials to degrade due to corrosion. To thoroughly investigate the corrosion processes and to determine oxidation states of metal components within the reaction products, we need special analytical tools. Grazing exit X-ray fluorescence (GEXRF) offers a non-destructive way to collect this information in sub-micrometre depth range.
In order to obtain structural information, such as regarding oxidation states or atomic/molecular geometric arrangement, the GEXRF approach can also be combined with the X-ray absorption spectroscopy (XAS) method. The position and energy sensitive detector, with 264x264 pixel detector area, provides information regarding the signal emitted from the sample as a function of the emission angle and thus allows depth-sensitive analysis. Furthermore, the data collected from samples of an incidence energy which can be controlled with a resolution of 0.5 eV provides XANES data to determine oxidation states.
We address the feasibility of our setup and provide a new optimization procedure (Bayesian Optimization and Gaussian Regression) to decrease measuring time. The results settle on a conceptual study on a reference sample (Cr-Oxide layer (300nm) on Cr layer (500nm) on Si wafer).
Getting more efficient – The use of Bayesian optimization and Gaussian processes at the BAMline
(2022)
For more than 20 years, BAM is operating the BAMline at the synchrotron BESSY II in Berlin Adlershof. During this time, the complexity of the setup and the amount of data generated have multiplied. To increase the effectiveness and in preparation for BESSY III, algorithms from the field of machine learning are increasingly used.
After a short introduction to BO and GP, the first example is the automatic alignment of our double multilayer monochromator (DMM).
The second example is the optimization of measurement time in XRF scanning.
News from the BAMline
(2022)
Time resolved in situ monitoring of mechanochemical transformations by X-ray spectroscopy (XAS)
(2021)
Mechanochemical reactions promise a new direction for environmentally benign preparation of materials, and has been dubbed by IUPAC as one of the 10 chemical innovations that will change our world. Despite this significant promise, very little is known about the mechanisms that drive mechanochemical transformations, posing significant barriers to realizing their full potential. To this end, there is growing need to follow mechanochemical reactions in situ and in real time. We here describe advances in the development and application of XAS methods to monitor material synthesis in real time under mechanochemical conditions. We demonstrate the generality of our approaches by describing mechanochemical syntheses of materials by both vibratory ball milling and by Resonant Acoustic Mixing (RAM), where a time resolution of 1 second is for a whole XAS spectrum was achieved. Moreover, we describe how spectroscopic methods can be coupled to diffraction-based approaches, thereby providing new dimensions in understanding mechanochemical synthesis.
Mechanochemical reactions promise a new direction for environmentally benign preparation of materials, and has been dubbed by IUPAC as one of the 10 chemical innovations that will change our world. Despite this significant promise, very little is known about the mechanisms that drive mechanochemical transformations, posing significant barriers to realizing their full potential. To this end, there is growing need to follow mechanochemical reactions in situ and in real time. We here describe advances in the development and application of XAS methods to monitor material synthesis in real time under mechanochemical conditions. We demonstrate the generality of our approaches by describing mechanochemical syntheses of materials by both vibratory ball milling and by Resonant Acoustic Mixing (RAM), where a time resolution of 1 second is for a whole XAS spectrum was achieved. Moreover, we describe how spectroscopic methods can be coupled to diffraction-based approaches, thereby providing new dimensions in understanding mechanochemical synthesis.
Time resolved in situ monitoring of mechanochemical transformations by X-ray spectroscopy (XAS)
(2021)
Mechanochemical reactions promise a new direction for environmentally benign preparation of materials, and has been dubbed by IUPAC as one of the 10 chemical innovations that will change our world. Despite this significant promise, very little is known about the mechanisms that drive mechanochemical transformations, posing significant barriers to realizing their full potential. To this end, there is growing need to follow mechanochemical reactions in situ and in real time. We here describe advances in the development and application of XAS methods to monitor material synthesis in real time under mechanochemical conditions. We demonstrate the generality of our approaches by describing mechanochemical syntheses of materials by both vibratory ball milling and by Resonant Acoustic Mixing (RAM), where a time resolution of 1 second is for a whole XAS spectrum was achieved. Moreover, we describe how spectroscopic methods can be coupled to diffraction-based approaches, thereby providing new dimensions in understanding mechanochemical synthesis.
Getting more efficient – The use of Bayesian optimization and Gaussian processes at the BAMline
(2022)
For more than 20 years, BAM is operating the BAMline at the synchrotron BESSY II in Berlin Adlershof. During this time, the complexity of the setup and the amount of data generated have multiplied. To increase the effectiveness and in preparation for BESSY III, algorithms from the field of machine learning are increasingly used.
In this paper, several examples in the areas of beamline alignment and measurement time optimization based on Bayesian optimization (BO) with Gaussian processes (GP) are presented. BO is a method for finding the global optimum of a function using a probabilistic model represented by a GP. The advantage of this method is that it can handle high-dimensional problems, does not depend on the initial estimate, and also provides uncertainty estimates.
After a short introduction to BO and GP, the first example is the automatic alignment of our double multilayer monochromator (DMM). To achieve optimal performance, up to three linear and two angular motor positions have to be optimized. To achieve this with a grid scan, at least 100^5 measurement points would be required. Assuming that all positions can be aligned independently, 100*5 points are still necessary. We show that with BO and GP less than 100 points are sufficient to achieve equal or better results.
The second example is the optimization of measurement time in XRF scanning. Here we will show the advantage of the BO GP approach over point-by-point scanning. As can be seen in Fig. 1, the number of points required and thus the measurement time can be reduced by a factor of 50, while the loss in image quality is acceptable. The advantages and limitations of this approach will be discussed.
Mechanochemical reactions promise a new direction for environmentally benign preparation of materials, and has been dubbed by IUPAC as one of the 10 chemical innovations that will change our world. Despite this significant promise, very little is known about the mechanisms that drive mechanochemical transformations, posing significant barriers to realizing their full potential. To this end, there is growing need to follow mechanochemical reactions in situ and in real time. We here describe advances in the development and application of XAS methods to monitor material synthesis in real time under mechanochemical conditions. We demonstrate the generality of our approaches by describing mechanochemical syntheses of materials by both vibratory ball milling and by Resonant Acoustic Mixing (RAM), where a time resolution of 1 second is for a whole XAS spectrum was achieved. Moreover, we describe how spectroscopic methods can be coupled to diffraction-based approaches, thereby providing new dimensions in understanding mechanochemical synthesis.
In this talk an overview about artificial intelligence/machine learning applications @BAMline is given. In the first part, the use of neural networks for the quantification of XRF measurements and the decoding of coded-aperture measurements are shown. Then it is shown how Gaussian processes and Bayesian statistics can be used to achieve an optimal alignment of the set-up and in general for optimization of measurements.