GO-RXR: Global Optimization of Resonant X-ray Reflectometry
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».