(Invited) Unraveling Electron-Phonon Coupling in 2D Materials in Momentum and Time with Ultrafast Electron Diffuse Scattering (UEDS)
Bibliographic record
Abstract
The nature of the couplings within and between lattice and charge degrees of freedom is central to an understanding of electrical and heat transport in materials. These interactions are essential to phenomena as diverse as superconductivity, charge density waves and carrier mobility in semiconductors and metals. Despite their fundamental role, detailed momentum-dependent information on the strength of electron-phonon coupling (EPC) and phonon-phonon coupling (PPC) across the entire Brillouin zone has proved to be very difficult to obtain. I will describe an emerging pump-probe technique, ultrafast electron diffuse scattering (UEDS), that provides such information from the perspective of the phonon system directly. Recent examples of the application of UEDS to layered (2D) materials will be the focus. First, in the thermoelectric material SnSe – a strongly polar semiconductor – we directly observe the phonon dressing processes that yield carrier localization and polaron formation with UEDS. In SnSe these phonon dressing dynamics are profoundly bimodal, with the fast (300 fs) process associated with the formation of a quasi-1D lattice distortion and a relatively large polaron and the slower (4 ps, an order of magnitude slower timescale) process associated with small polaron formation. The observations in SnSe are consistent with electron and hole polarons being different sizes, or the process of polaron formation being instrincially bimodal for both carriers in a manner reminiscent of Lars Onsager’s inverse snowball effect. Second, the extension of UEDS to a MoS2 monolayer heterostructure will be demonstrated. These results reveal substrate dielectric screening of the electron-phonon interaction within the monolayer as well as the monomentum-dependent carrier-phonon equilibration. These results are compared directly against ab-initio simulations of these processes Figure 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".