Asymptotic Expansion for Electrostatic Embedding Integrals in QM/MM Calculations
Bibliographic record
Abstract
In QM/MM studies with large MM regions, the calculation of electrostatic embedding integrals can become a computational bottleneck. To overcome this problem, an asymptotic expansion for nuclear attraction-type integrals is developed. As a result, the long-range interactions between the QM and MM atoms reduce to atom-centered multipole moment-like expansions. The algorithm uses a natural spatial division of the molecular structure. To further improve the computational performance, a cutoff radius for the multipole moment-like expansion is introduced. The new code was validated and benchmarked with deMon2k/CHARMM QM/MM calculations on an RNA polymerase II model with almost 350 000 atoms. It is shown that the computational time for the calculation of the embedding integrals in this system can be reduced below 200 s on a small parallel architecture (eight cores) without a loss of accuracy.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".