Electrostatic Potential Derived Atomic Charges for Periodic Systems Using a Modified Error Functional
Bibliographic record
Abstract
A method to generate electrostatic potential (ESP) derived atomic charges in crystalline solids from periodic quantum mechanical calculations, termed the REPEAT method, is presented. Conventional ESP fitting procedures developed for molecular systems, in general, will not work for crystalline systems because the electrostatic potential in periodic systems is ill-defined up to a constant offset at each spatial position. In this work the problem is circumvented by introducing a new error functional which acts on the relative differences of the potential and not on its absolute values, as it is currently done with molecular ESP charge derivation methods. We formally demonstrate that the new functional reduces to the conventional error functional used in molecular ESP approaches when the simulation box of the periodic calculation becomes infinitely large. Several tests are presented to validate the new technique. For the periodic calculation of isolated molecules, the REPEAT charges are found to be in good agreement with those determined with established molecular ESP charge derivation methods. For siliceous sodalite, it is demonstrated that conventional molecular ESP approaches generate 'unphysical' charges, whereas the REPEAT method produces charges that are both chemically intuitive and consistent between different periodic electronic structure packages. The new approach is employed to generate partial atomic charges of various microporous materials and compared to both experimentally derived and molecular fragment ESP charges. This method can be used to generate partial atomic charges to be used in simulations of microporous and nanoporous materials, such as zeolites and metal organic framework materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".