Critically Assessing the Application of Quantum Corrections to Classical Thermal Conductivity Predictions
Bibliographic record
Abstract
Quantum corrections can be used to map the thermal conductivity predicted in a classical framework [e.g., a molecular dynamics (MD) simulation] to a corresponding value in a quantum system. This procedure is accomplished by equating the total energies and energy fluxes of the classical and quantum systems. The validity of these corrections is questionable because they are introduced in an ad hoc manner and are not derived from first principles. In this work, the validity of these quantum corrections is examined by comparing the thermal conductivity of Stillinger-Weber silicon calculated using a full quantum mechanical treatment to a quantum-corrected value predicted from a classical framework between temperatures of 10 K and 1000 K. The quantum and classical predictions are obtained using anharmonic lattice dynamics calculations. We find discrepancies between the quantum-corrected predictions and the quantum predictions obtained directly. We investigate the causes of these discrepancies and from our findings, conclude that quantum thermal conductivities cannot be predicted by applying simple corrections to the values obtained from a purely classical framework.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".