Defect-resolved scattering in quantum materials : a scanning tunneling microscope study with algorithmic multi-channel deconvolution
Bibliographic record
Abstract
Point defects in quantum materials, once regarded as imperfections, are now recognized as central players in shaping electronic properties. They can scatter quasiparticles, modify local order, and in some cases host entirely new states of matter. Scanning tunneling microscopy provides a uniquely powerful window into these effects, combining atomic-scale spatial resolution with spectroscopic access to electronic structure. Yet in practice, the contributions of different defect types are typically entangled, obscuring their individual roles. In this thesis, I develop two complementary approaches to address this problem. First, I establish a statistical framework to estimate the global densities and their associated uncertainties of each type of defect directly from scanning tunneling microscopy topographs. Applied to the ultra-pure semimetal PtSn₄, this approach demonstrates how nanoscale imaging can yield quantitative information about the populations of distinct defect species across macroscopic crystals. Second, I introduce a new analysis method—multi-channel sparse blind deconvolution tailored for scanning tunneling data—designed to disentangle overlapping quasiparticle interference patterns into defect-specific scattering fingerprints. Benchmarks on synthetic datasets identified the conditions required for reliable reconstruction, and application to experimental data produced defect-resolved interference patterns in several systems. Full success was achieved on Ag(111) and ZrSiTe, partial success on LiFeAs, and failure on PtSn₄, where the limitations of the method matched expectations from simulations. In ZrSiTe in particular, the defect-resolved interference pattern for the Zr₂ defect revealed clear evidence of floating-band scattering features, which had previously been reported as absent.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".