Two‐Temperature Pair Potentials and Phonon Spectra for Simple Metals in the Warm Dense Matter Regime
Bibliographic record
Abstract
Abstract We develop ion‐ion pair potentials for Al, Na and K for densities and temperatures relevant to the warm‐densematter (WDM) regime. Furthermore, we emphasize non‐equilibrium states where the ion temperature Ti differs from the electron temperature Te. This work focuses mainly on ultra‐fast laser‐metal interactions where the energy of the laser is almost exclusively transferred to the electron sub‐system over femtosecond time scales. This results in a two‐temperature system with Te > Ti and with the ions still at the initial room temperature Ti = Tr. First‐principles calculations, such as density functional theory (DFT) or quantum Monte Carlo, are as yet not fully feasible for WDM conditions due to lack of finite‐T features, e.g. pseudopotentials, and extensive CPU time requirements. Simpler methods are needed to study these highly complex systems. We propose to use two‐temperature pair potentials Uii(r, Ti, Te) constructed from linear‐response theory using the non‐linear electron density n (r) obtained from finite‐T DFT with a single ion immersed in the appropriate electron fluid. We compute equilibrium phonon spectra at Tr which are found to be in very good agreement with experiments. This gives credibility to our non‐equilibrium phonon dispersion relations which are important in determining thermophysical properties, stability, energy‐relaxation mechanisms and transport coefficients. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".