Acceleration of Thermochimica Calculations in Bison
Bibliographic record
Abstract
The performance of the open-source thermochemical software library Thermochimica has been enhanced by developing a re-initialization algorithm to make use of data from previous calculations to reduce the number of convergence steps in the Gibbs energy minimization procedure. This algorithm has been tested in the context of stand-alone Thermochimica calculations, and speedups in the range of 2x-3x achieved for cases with chemistries resembling those of irradiated nuclear fuels. Routines to make use of this re-initialization procedure have been implemented in Bison, in which each node uses the results of the previous calculation at that node as the initial conditions for the following Thermochimica call. Examples based on calculating the diffusion of oxygen in UO$_2$ LWR fuel in 1D and 3D have been tested, with speedups up to 7.38x demonstrated.
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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) | 0.007 | 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 teacher head, 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".