First principles calculation of thermo-mechanical properties of thoria using Quantum ESPRESSO
Bibliographic record
Abstract
In this work, we have used Quantum ESPRESSO (QE), an open source first principles code, based on density-functional theory, plane waves, and pseudopotentials, along with quasi-harmonic approximation (QHA) to calculate the thermo-mechanical properties of thorium dioxide (ThO[Formula: see text]. Using Python programming language, our group developed qe-nipy-advanced, an interface to QE, which can evaluate the structural and thermo-mechanical properties of materials. We predicted the phonon contribution to thermal conductivity ([Formula: see text] using the Slack model. We performed the calculations within local density approximation (LDA) and generalized gradient approximation (GGA) with the recently proposed version for solids (PBEsol). We employed a Monkhorst-Pack [Formula: see text] k-points mesh in reciprocal space with a plane wave cut-off energy of 150 Ry to obtain the convergence of the structure. We calculated the dynamical matrices of the lattice on a [Formula: see text] mesh. We have predicted the heat capacity, thermal expansion and the phonon contribution to thermal conductivity, as a function of temperature up to 1400[Formula: see text]K, and compared them with the previous work and known experimental results.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".