Density Functional Theory: Past, present,... future?
Bibliographic record
Abstract
In little more than 20 years, the number of applications of the density functional (DF) formalism in chemistry and materials science has grown in astonishing fashion. The number of publications alone shows that DF calculations are a huge success story, and many younger colleagues are surprised to learn that the real breakthrough of density functional methods, particularly in chemistry, began only after 1990. This is indeed unexpected, because the origins are usually traced to the papers of Hohenberg, Kohn, and Sham more than a quarter of a century earlier. Olle Gunnarsson and I reviewed the DF formalism, its applications, and prospects for this journal as we saw them in 1989, and I argued shortly afterwards in Angewandte Chemie that combining such calculations with molecular dynamics should lead to an efficient way of finding molecular structures. Here I take that time (1990) as fixed point, review the development of density related methods back to the early years of quantum mechanics and follow their breakthrough after 1990. The two examples from biochemistry and materials science are among the many current applications that were simply beyond our dreams in 1990. I discuss the reasons why- after two decades of rapid expansion- some of the best-known practitioners in the field are concerned about its future.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".