Lightwave-controlled band engineering in quantum materials
Bibliographic record
Abstract
Abstract In recent years, the stacking and twisting of atom-thin structures with matching crystal symmetry has provided a unique handle to create new superlattice structures where new properties emerge1,2. In parallel, control over the temporal characteristics of strong light fields has allowed to manipulate coherent electron transport in such atom-thin structures on sub-laser-cycle timescales3,4. Here, we demonstrate a tailored lightwave-driven analogue to twisted layer stacking. Tailoring the spatial symmetry of the light waveform to that of the lattice of a hexagonal boron nitride monolayer, and twisting this waveform results in optical control of time-reversal symmetry breaking5, and the realization of the topological model of Haldane6,7 in the laser-dressed 2D insulating crystal. Further, the parameters of the effective Haldane-type Hamiltonian are controlled by the rotating light waveform, enabling ultrafast switching between band structure configurations and unprecedented control over the magnitude, location, and curvature of the band gap. A resultant asymmetric population at complementary quantum valleys leads to a measurable valley Hall current8, detected via optical harmonic polarimetry. The universality and robustness of our scheme opens the way to band engineering on the fly, unlocking the possibility to create few-femtosecond switches of quantum degrees of freedom.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".