Watching a solid shake itself apart: an atomic view of melting
Bibliographic record
Abstract
The picosecond barrier to high brightness electron pulses has been broken. Electron diffraction harbors great potential for providing atomic resolution to structural changes at critical points — a real-time view of atomic motions during structural transitions. Femtosecond electron pulses of sufficient number density to execute nearly single-shot structure determinations are needed. This requirement places severe constraints on the electron pulse propagation. A new photoactivated electron gun design has been developed based on an N-body numerical simulation and mean-field calculation of the electron wavepacket propagation that is capable of less than 600 femtosecond electron pulses with high enough brightness to provide structural details in the small shot number limit. Time-resolved diffraction studies with this new instrument have focused on strongly driven solid-liquid phase transitions of aluminum as a model problem of a structural transition. The signal to noise and available diffraction orders were sufficiently high to give direct access to fluctuations leading to the disordering or melting process and the associated radial distribution function. This work gives atomic level details of a solid-liquid phase transition, i.e., we can literally watch the atoms move during melting. The promise of atomically resolving transition state processes is at hand and applications along this line will be discussed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".