Stable BaCl solid at high pressure: Prediction and characterization using first principles approach
Bibliographic record
Abstract
Industrial processes involving the manufacture of heat treatment salts such as BaCl at high pressures are becoming possible. Hence, there is a need to search for a specific form of BaCl with excellent thermal properties. Motivated by this, the potential energy surface of BaCl is extensively explored using the unbiased particle swarm-intelligence optimization algorithm to uncover a global minimum enthalpy phase of BaCl within the pressure range that was recently experimentally explored. Previously predicted phases were confirmed during the structure search. Furthermore, the orthorhombic Pnma form of BaCl is predicted to be more stable and energetically more favorable than the previously predicted R-3m phase in the pressure range of ∼10–15 GPa. The electronic and thermal properties of the newly discovered phase are extensively studied using first principles calculations. In the pressure range of interest, Pnma BaCl is metallic and nonmagnetic. More so, the solution of the Boltzmann Transport Equation unravels promising thermal properties, which make Pnma BaCl a good candidate for heat management in high temperature systems. We found the overall Grüneisen parameters in Pnma BaCl to range between 0.963 and 0.995 and the lattice thermal conductivity at 300 K to be 53.7 W m−1 K−1. We also found that Pnma BaCl exhibits anisotropy that we observed is constant in all directions explored.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".