Time-Dependent Density Functional Theory for Atomic Collisions: A Progress Report
Bibliographic record
Abstract
In this paper, the current status of time-dependent density functional theory (TDDFT)-based calculations for ion–atom collision problems is reviewed. Most if not all reported calculations rely on the semiclassical approximation of heavy particle collision physics and the time-dependent Kohn–Sham (TDKS) scheme for computing the electronic density of the system. According to the foundational Runge–Gross theorem, all information available about the electronic many-body system is encoded in the density; however, in practice it is often not known how to extract it without resorting to modelling and approximations. This is in addition to a necessarily approximate implementation of the TDKS scheme due to the lack of precise knowledge about the potential that drives the equations. Notwithstanding these limitations, an impressive body of work has been accumulated over the past few decades. A sample of the results obtained for various collision systems is discussed here, in addition to the formal underpinnings and theoretical and practical challenges of the application of TDDFT to atomic collision problems, which are expounded in mostly nontechnical terms. Open problems and potential future directions are outlined as well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".