Ph3pyWF: An automated workflow software package for ceramic lattice thermal conductivity calculation
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces Ph3pyWF, a Python software package we designed to facilitate high-throughput analysis of lattice thermal conductivity in ceramic materials. The user interface caters to individuals with varying expertise, accommodating both novices and experts in the field. For beginners, only the initial structure file is required as input, as the software automatically populates other necessary parameters. Advanced users can customize numerous procedure parameters to suit their specific research needs. At its core, Ph3pyWF aims to establish an efficient data exchange and task management system. This paper elucidates the design details of the software package and presents several examples of its application to oxide ceramics , showcasing its general applicability and practicality in the analysis of lattice thermal conductivity . Program Summary Program title: Ph3pyWF CPC Library link to program files: https://doi.org/10.17632/487gf74mgh.1 Developer's repository link: https://github.com/MatFrontier/ph3pywf Licensing provisions: MIT Programming language: Python 3 External routines/libraries: Phonopy, Phono3py, Atomate, FireWorks, pymatgen Nature of problem: The calculation of lattice thermal conductivity using the first-principles method necessitates a multitude of interdependent subprocesses . Manually executing and managing such a collection of subprocesses proves to be inefficient and prone to errors. Solution method: Employing scientific workflow framework to automate the lattice thermal conductivity calculation process. Providing a near-turnkey solution with simpler management interface to users.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it