Towards engineered branch placement: Unreal™ match between vapour-liquid-solid glancing angle deposition nanowire growth and simulation
Bibliographic record
Abstract
The vapour-liquid-solid glancing angle deposition (VLS-GLAD) process is capable of producing complex nanotree structures with control over azimuthal branch orientation and height. We have developed a thin film growth simulation including ballistic deposition, simplified surface diffusion, and droplet-mediated cubic crystal growth for the VLS-GLAD process using the UnrealTM Development Kit. The use of a commercial game engine has provided an interactive environment while allowing a custom physics implementation. Our simulation's output is verified against experimental data, including a volumetric film reconstruction produced using focused ion beam and scanning-electron microscopy (SEM), crystallographic texture, and morphological characteristics such as branch orientation. We achieve excellent morphological and texture agreement with experimental data, as well as qualitative agreement with SEM imagery. The simplified physics in our model reproduces the experimental films, indicating that the dominant role flux geometry plays in the VLS-GLAD competitive growth process responsible for azimuthally oriented branches and biaxial crystal texture evolution. The simulation's successful reproduction of experimental data indicates that it should have predictive power in designing novel VLS-GLAD structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".