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.
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 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) | 0.095 | 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; both teacher heads agree on what is shown here.
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".