Base de données de configurations atomiques sur le silicium et la silice
Bibliographic record
Abstract
This collection constitutes a quantum mechanics database developed for the training of machine learning potentials. It was designed and generated as part of a joint modeling project involving silicon, silica, and oxygen. Thousands of quantum mechanical calculations were carried out using the Quantum ESPRESSO software to build this database. Each file with the .out extension contains essential information such as the simulation box lattice parameters, the number of atoms, the total energy, the atomic forces, and the stress tensor. These data are extracted to train machine learning-based interatomic potentials. The subset of data related to oxygen also includes files in .cfg format, which contain similar information: the simulation box, the number of atoms, energy, forces, and stress. From this database, one can train a machine learning potential or extract atomic coordinates in order to recompute quantum mechanical calculations using exchange-correlation (XC) functionals of choice. In the case of direct training, it is recommended to optimize the database beforehand to select a representative portion based on the specific needs of the model or application
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.022 |
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".