6 Materialchemie
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We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials.
McSAS3 is a refactored software package for fitting large batches of (X-ray or Neutron) scattering data. It uses a Monte-Carlo acceptance-rejection algorithm to optimize model parameters - ideal for analysis of size-disperse scatterers.
The refactored code can exploit multiprocessing, traceably stores (multiple) results in the output file, and allows for re-histogramming of previous optimizations. Besides analysis of large batches, it can also be integrated in automated data processing pipelines.
The live demonstration will show how to use the software, what its limitations are, and what outcomes can look like for batches of results.
Simulates X-ray and Neutron scattering patterns from arbitrary shapes defined by STL files.
Features:
- Uses multithreading to compute a number of independent solutions, then uses the variance of the results to estimate an uncertainty on the output.
- Can be launched from the command line using an excel sheet to define settings, or from a jupyter notebook.
- Outputs scattering patterns in absolute units if the contrast is set.
- A Gaussian size distribution is available, where the relative scaling of objects for each repetion can be varied. Recommended to be used with limited width (max. 10%) to avoid artefacts.
- Writes results with settings to an archival HDF5 file.
Application examples:
This software has been used in several studies to date. For example, it has been used here to simulate a model scattering pattern for a cuboid shape, which was then fed forward into the McSAS3 analysis program for analyzing scattering patterns of polydisperse cuboids. A second use is here, where it was used for the modeling of flattened helices. In this paper, scattering pattern features could be matched with particular morphological changes in the structure. Lastly, this paper has an example where it was used to validate the analytical analysis model, and explore the realistic limits of application of the analytical model.
The SPONGE
(2020)
No matter whether you are doing X-ray diffraction or scattering, at wide or small angles, in a lab or at a synchrotron, you will need a bespoke sequence of up to twenty different corrections before it gets about right (at our latest count). Our library now does this automatically and reliably to get you the data you deserve – in absolute units and with uncertainty estimates – without the pain. This talk will highlight the development of the modular library, the sequence and its foundation, show its applicability to real-world datasets, and highlight a possible way forward
Small-angle scattering (SAS) offers a reliable route to characterize the nanostructure of large amounts of material with a minimum of tedium, for example, easily extracting size distributions and volume fractions. There are a variety of analysis programs available while the evaluation of SAS measurements has been dominated by the classical curve fitting approach. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for SAS data analysis and has been available and used for many years. The latest developments will be presented and a scattering function of a mass fractal model of branched polymers in solution is provided as an example for implementing a plug-in.
Alternatively to classical curve fitting, part two presents the latest developments of the user-friendly open-source Monte Carlo regression package McSAS. The form-free Monte Carlo nature of McSAS means, it is not necessary to provide further restrictions on the mathematical form of the parameter distribution: without prior knowledge, McSAS is able to extract complex multimodal or odd- shaped parameter distributions from SAS data. The headless mode is presented by an example of operation within interactive programming environments such as a Jupyter notebook.
The test material EDS-TM001 together with an accompanying software package, “EDX spectrometer check”, have been made available in 2009 by BAM to be employed by EDS (energy-dispersive X-ray spectrometer) users to check the performance of an EDS attached to the SEM. Particularly for test laboratories operating under accreditation schemes like ISO/IEC 17025, a periodical control of the critical instrumental parameters in end-user laboratories is required. With EDS-TM001 or EDS-TM002 (second generation) test material, this periodical check is simplified to the acquisition of only one 10 kV spectrum. The software “EDX spectrometer check” is destined to evaluate automatically this spectrum and determine the performance of the EDS in terms of energy resolution and calibration, as well as possible alteration of low-energy Efficiency due to detector contamination. Energy resolution can be compared with the specified values according to the international ISO standard ISO 15632:2012. EDS-TM is a synthetic material consisting of a 6 μm thick layer of C, Al, Mn, Cu and Zr deposited on a steel (in case of EDS-TM001) or silicon (in case of EDS-TM002) substrate. The chemical composition of EDS-TM was chosen such as to give nearly equal intensities of the low energy lines in a 10 kV spectrum, thus, making it very sensitive against spectrometer efficiency changes. Meanwhile, about 150 laboratories use the EDS-TM001 or EDS-TM002 test material for the periodical check of their EDS. A detailed description of the test material and software together with examples of application was published recently. New results and gained experiences will be presented as well. When the FWHM of the X-ray lines in the EDS-TM spectrum are determined, the spectrum background must be subtracted accurately. The applied physical background subtraction procedure is robust and takes into account the transmission of the detector window. While the previous version considers only Moxtek AP windows, the new version includes selection of silicon Nitride window and the case of windowless detector. Moreover, the new version allows importing of spectra in Bruker spx-format and EMSA/MSA files from EDAX TEAM software.
Windowless detectors have been also tested demonstrating long-term stability after repeated heating and cooling cycles. In this case, the appropriate Background subtraction is decisive for accurate detector characterization. Detailed results will be presented.