An Interface to Quantum Espresso
Bibliographic record
Abstract
Our project aims at providing the materials engineering fraternity with a simple and effective interface using ipython to operate Quantum ESPRESSO (QE), an open source code for materials simulation. QE is a first principles code using density functional theory, plane waves and pseudo potentials; it has ability to predict material properties. Ipython notebook interface uses the scope of the following libraries; Atomic Simulation Environment (ASE), matplotlib, scipy, numpy, pyspglib, elastic and newly developed library: QE-nipy-advanced to predict the properties. QE-nipy-advanced is the latest version of QE-nipy. The latest version incorporates features that can take care of all the input parameters supported by PWscf and PHonon packages of Quantum ESPRESSO. Thermo-mechanical properties of some nuclear materials with different magnetic and metallic behavior has been studied using the QE-nipy-advanced, but in here we demonstrate the thermo-mechanical properties of non-magnetic insulator like silicon carbide and thoria, which are materials for future nuclear reactor applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.028 |
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".