Global Optimization of Resonant X-ray Reflectometry Models: Analysis of Perovskite Oxide Heterostructures
Bibliographic record
Abstract
Resonant x-ray reflectometry is an emerging synchrotron technique used to characterize the depth-dependent structure of quantum materials. The main challenge impeding the success of resonant x-ray\nreflectometry is the extreme difficulty of analyzing the data because the process involves both large-scale\ncomputational quantum mechanics simulations and the fitting of many independent variables. This leads\nto prolonged analysis periods that require a significant amount of engagement. As a part of this thesis, a\nnew data analysis software named Global Optimization of Resonant X-ray Reflectometry was developed for\nresearchers to use to more effectively analyze resonant x-ray reflectometry data and to mitigate some of these\nchallenges. It has been shown throughout this thesis that multiple features in the software have been able\nto ease the data analysis process. A large focus will be put on the customizable objective function because\nthe boundaries and weights and total variation features have been proven to be integral components to the\nsuccess of the software.\nThe developed software was used to analyze resonant x-ray reflectometry (RXR) data of the catalyst\nLa0.7Sr0.3MnO3/SrTiO3 (LSMO/STO) for electrochemical water splitting. Resonant x-ray reflectometry is\nused to develop a new enhanced understanding of the structural, electronic, and magnetic depth profiles of\nthin LSMO films by characterizing the depth-dependence of such materials for varying film thickness and\nmeasurement temperature. The results provide evidence of a magnetically dead layer at the surface and\ndemonstrate a decrease in the magnetic moment near the Curie temperature. These findings are significant\nbecause they help understand the mechanisms involved in the oxygen evolution reaction and methods that\ncan be used to improve water splitting efficiency.\nResonant x-ray reflectometry is also employed to study the thickness relationship between film thickness\nand the presence of ferromagnetism in the LaMnO3/SrTiO3 heterostructure. The electronic reconstruction\ndue to polar catastrophe is the leading theory for the mechanism involved in the magnetic phase transition,\nbut this study provides a new understanding of the emergence of magnetism in ultra-thin films of LaMnO3\nthat contradict the polar catastrophe mechanism. Notably, ferromagnetism is detected below the critical\nthickness, as supported by density functional theory calculations. Moreover, this study provides evidence\nthat the magnetic moment is related to the distortions in the material. It is possible that octahedral distortions are formed and are the proposed cause for the observed ferromagnetism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".