Simple pair-potentials and pseudo-potentials for warm-dense matter applications
Bibliographic record
Abstract
We present computationally simple parameter-free pair potentials useful for solids, liquids, and plasmas at arbitrary temperatures. They successfully treat warm-dense matter (WDM) systems like carbon or silicon with complex tetrahedral or other structural bonding features. Density functional theory asserts that only one-body electron densities and one-body ion densities are needed for a complete description of electron–ion systems. Density functional theory (DFT) is used here to reduce both the electron many-body problem and the ion many-body problem to an exact one-body problem, namely, that of the neutral pseudoatom (NPA). We compare the Stillinger–Weber (SW) class of multi-center potentials, the embedded-atom approaches, and N-atom DFT, with the one-atom DFT approach of the NPA to show that many-ion effects are systematically included in this one-center method via one-body exchange-correlation functionals. This computationally highly efficient one-center DFT-NPA approach is contrasted with the usual N-center DFT calculations that are coupled with molecular dynamics simulations to equilibrate the ion distribution. Comparisons are given with the pair-potential parts of the SW, “glue” models, and the corresponding NPA pair-potentials to elucidate how the NPA potentials capture many-center effects using single-center one-body densities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".