Numerical methods in quantum chemistry to accelerate SCF convergence and calculate partial atomic charges
Bibliographic record
Abstract
In this thesis, we devise a series of minimization based methods to reduce the numbers of iterations needed to achieve convergence in SFC calculations. These methods are based on building linear combinations of Fock and Density matrices from previous iterations. Our techniques help to converge some systems for which established methods like DIIS and ADIIS fail. We also propose a scheme that combines our methods with DIIS. For some systems, this scheme reduces the number of iterations needed to reach convergence by more than 90\%. We also propose a method for assigning partial atomic charges in molecules. This method requires the division of the covalent part of the molecular density in partitions that take into account the ionic densities, the atomic number and the number of core/valence electrons in each atom. For small basis sets, our method provides atomic charges that are very similar in quality to those of natural population analysis, but with a lesser computational cost and a much more straightforward implementation. Finally, we collaborated with Dr. Ray Anderson in the identification of the absolute configuration of a series of chiral organic compounds. This was achieved by comparing the experimental electronic circular dichroism spectra with a theoretical one calculated with time-dependent density functional theory.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".