Isochoric, isobaric and ultrafast conductivities of aluminum, lithium and carbon in the warm dense matter (WDM) regime
Bibliographic record
Abstract
We study the conductivities $σ$ of (i) the equilibrium isochoric state ($σ_{\rm is}$), (ii) the equilibrium isobaric state ($σ_{\rm ib}$), and also the (iii) non-equilibrium ultrafast matter (UFM) state ($σ_{\rm uf}$) with the ion temperature $T_i$ less than the the electron temperature $T_e$. Aluminum, lithium and carbon are considered, being increasingly complex warm dense matter (WDM) systems, with carbon having transient covalent bonds. First-principles calculations, i.e., neutral-pseudoatom (NPA) calculations and density-functional theory (DFT) with molecular-dynamics (MD) simulations, are compared where possible with experimental data to characterize $σ_{\rm ic}, σ_{\rm ib}$ and $σ_{\rm uf}$. The NPA $σ_{\rm ib}$ are closest to the available experimental data when compared to results from DFT+MD, where simulations of about 64-125 atoms are typically used. The published conductivities for Li are reviewed and the value at a temperature of 4.5 eV is examined using supporting X-ray Thomson scattering calculations. A physical picture of the variations of $σ$ with temperature and density applicable to these materials is given. The insensitivity of $σ$ to $T_e$ below 10 eV for carbon, compared to Al and Li, is clarified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".