The classical‐map hyper‐netted‐chain (CHNC) method and associated novel density‐functional techniques for warm dense matter
Bibliographic record
Abstract
Abstract The advent of short‐pulse lasers, nanotechnology, as well as shock‐wave techniques have created new states of matter (e.g., warm dense matter) that call for new theoretical tools. Ion correlations, electron correlations, as well as bound states, continuum states, partial degeneracies and quasi‐equilibrium systems need to be addressed. Bogoliubov's ideas of timescales can be used to discuss the quasi‐thermodynamics of nonequilibrium systems. A rigorous approach to the associated many‐body problem turns out to be the computation of the underlying pair‐distribution functions gee, gei, and gii, that directly yield nonlocal exchange‐correlation potentials, free energies etc., valid within the timescales of each evolving system. An accurate classical map of the strongly‐quantum uniform electron‐gas problem given by Dharma‐wardana and Perrot is reviewed. This replaces the quantum electrons at T = 0 by an equivalent classical fluid at a finite temperature Tq, and having the same correlation energy. The classical map is used with classical molecular dynamics (CMMD) or hyper‐netted‐chain integral equations (CHNC) to determine the pair‐distribution functions (PDFs), and hence their thermodynamic and linear transport properties. The CHNC is very efficient for calculating the PDFs of uniform systems, while CMMD is more adapted to nonuniform systems. Applications to 2D and 3D quantum fluids, Si metal‐oxide‐field‐effect transistors, Al plasmas, shock‐compressed deuterium, two‐temperature plasmas, pseudopotentials, as well as calculations for parabolic quantum dots are reviewed. © 2011 Wiley Periodicals, Inc. Int J Quantum Chem, 2012
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".