GO-RXR: Global Optimization of Resonant X-ray Reflectometry
Bibliographic record
Abstract
GO-RXR: Global Optimization of Resonant X-ray Reflectometry (JOSS) Authors Lucas Korol, Robert J. Green, Jesus P. Curbelo, and Raymond J. Spiteri Abstract Resonant x-ray reflectometry (RXR) is a cutting-edge synchrotron technique used to characterize the depth-dependent structure of quantum materials [1,2]. However, the main challenge impeding the success of RXR data analysis lies in the complexity of the workflow, driven by complicated model construction and the fitting of numerous parameters. This workflow complexity results in prolonged analysis periods that demand significant engagement from researchers. In response to these challenges, the Global Optimization of Resonant X-ray Reflectometry (GO-RXR) software emerged from rigorous development efforts as a main contribution from the work done in [3]. GO-RXR streamlines data analysis, enhances visualization, and reduces the specialized expertise required, offering researchers a more efficient means to analyze RXR data. This paper presents an overview of GO-RXR, highlighting its functionality, example use-cases, and impact in materials science research. Through its comprehensive approach and user-friendly design, GO-RXR offers researchers an efficient tool for analyzing RXR data, facilitating breakthroughs in understanding complex material systems. Additionally, publications and ongoing research utilizing GO-RXR underscore its versatility and impact in advancing scientific exploration. Main Features Graphical User Interface (GUI): Introduced a user-friendly interface for streamlined interaction with the software. Sample Definition as Compound-Profile: Enabled detailed sample configurations to enhance analysis precision. Adaptive Layer Segmentation: Implemented dynamic segmentation for improved layer analysis. Internal Database of Form Factors: Integrated a comprehensive database to facilitate form factor selection within project files. Magnetism Capabilities: Added support for magnetic sample analysis, broadening research applications. Compatibility with ReMagX: Ensured seamless loading of datasets from ReMagX for enhanced interoperability. Improvements Enhanced Documentation: Provided detailed installation guides and user manuals to assist users in setup and utilization. Cross-Platform Support: Tested and validated functionality on both Linux and Windows (via WSL) systems. **Authors' ORCID have been updated in the paper manuscript. Bug Fixes Data Fitting Execution: Resolved an issue where data fitting would execute the script regardless of selection. Known Issues PyQt5 Conflicts on Ubuntu: Users may encounter conflicts with the PyQt5 package during installation. To resolve, install necessary dependencies and remove existing PyQt5 installations from the virtual environment. PyQt5 Conflicts on WSL: Users may experience issues on Windows 11 Education. Updating WSL to the latest version can resolve these conflicts. Upgrade Notes Follow the updated installation instructions in the README to ensure compatibility with your system. This release marks the initial public availability of GO-RXR, providing researchers with a robust tool for global optimization in resonant X-ray reflectometry. What's Changed Update orcid by @jpcurbelo in https://github.com/lucaskorol21/GO-RXR/pull/48 Full Changelog: https://github.com/lucaskorol21/GO-RXR/compare/v1.0.2...v1.0.3 References Keimer, B., & Moore, J. (2017). The physics of quantum materials. Nature Physics, 13(1045-1055). https://doi.org/10.1038/nphys4302 Green, R. J., Sutarto, R., He, F., Hepting, M., Hawthorn, D. G., & Sawatzky, G. A. (2020). Resonant Soft X-ray Reflectometry and Diffraction Studies of Emergent Phenomena in Oxide Heterostructures. Synchrotron Radiation News, 33(2), 20-24. https://doi.org/10.1080/08940886.2020.1725797 Korol, L. (2023). Global optimization of resonant x-ray reflectometry models: Analysis of perovskite oxide heterostructures (Master's thesis, University of Saskatchewan). Saskatoon, Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".