Systematic improvement of approximations with smooth models of the Coulomb potential
Bibliographic record
Abstract
Orbital-based methods for electronic-structure calculations are limited to atoms or molecules with up to about 50 electrons. This limitation comes from the requirement of a long expansion in basis functions to approximate correctly the wave function. Replacing the Coulomb interaction with a smooth model potential has two main consequences: first, the wave function becomes cuspless and the expansion in basis functions converges more rapidly, and second, the smooth potential describes a weaker interaction at the electronic coalescence point, which leads to the loss of accuracy. This work explores whether one can construct models with smooth, non-singular, potentials, but without compromising accuracy. The key idea is to use extrapolation procedures to predict the energy for the Coulomb interaction from a sequence of (cheaper) calculations for smooth potentials. By replacing the Coulomb electron-electron interaction with a smooth potential, using the semi-stochastic heat-bath configuration interaction method (SHCI) to select key configurations, and extrapolating to the limiting (non-smoothed) Coulomb potential, we were able to retain the accuracy of full configuration interaction (FCI) calculations, at reduced computational cost.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".