Combining spin-orbit coupling and multi-orbital interactions: a recipe for novel magnetism and superconductivity
Bibliographic record
Abstract
This thesis explores a multi-orbital model with a strong spin-orbit coupling where interactions are tuned via a compressive strain.The platform used to explore this type of physics is the perovskite iridate Sr 2 IrO 4 .Undoped and unstrained, this iridate compound is a spin-orbit coupled antiferromagnet.Under doping, Sr 2 IrO 4 has been predicted to host superconductivity.Applying a compressive strain to the compound tunes the dispersion of electrons in each orbital and consequently the interactions between electrons.In a model considering strain and doping, iridate physics is shown to encompass the two cases of either the interacting order being dominated by spin-orbit physics or multi-orbital interactions and spin-orbit coupling being of comparable size.This thesis focuses on modeling magnetism and superconductivity.Firstly, the magnetic order parameters are modeled with a mean field approximation.For undoped Sr 2 IrO 4 under compressive strain the multi-orbital nature of the order is determined, and a strain-induced phase transition takes place.An external magnetic field is included to further determine signatures of the order.Secondly, superconductivity is modeled with an effective interaction calculated via the random phase approximation (RPA).For realistic parameter values for doped Sr 2 IrO 4 a strain-induced superconducting order is found to be possible.Considering a wider range of parameters reveals a theoretical phase diagram rich with magnetic and superconducting orders.As the compressive strain is increased, several types of magnetic fluctuations compete.For the found novel superconducting orders a classification of symmetries as well as determination of topological properties is performed.Strain in the iridates is thus not only shown to be a useful tool to expand a possible superconducting region at high spin-orbit coupling.It is also a good tool to explore the complex system of underlying interactions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".