Split-Charge Equilibration Parameters for Generating Rapid Partial Atomic Charges in Metal–Organic Frameworks and Porous Polymer Networks for High-Throughput Screening
Bibliographic record
Abstract
The split-charge equilibration (SQE) method was parametrized to reproduce the quantum mechanical, electrostatic potential (ESP) in an atomistically and topologically diverse training set of 559 metal–organic frameworks (MOFs) and 45 porous polymer networks (PPNs). The training set contained a total of 17 elements and 31 unique element–element bonds, 13 inorganic SBUs, 101 organic SBUs, and 30 functional groups. The split-charge equilibration MOF electrostatic-potential-optimized, or SQE-MEPO, method was validated against a set of 585 (520 MOFs and 65 PPNs) that were not part of the training set by comparing the derived ESP to the quantum mechanical ESP and comparing the computed CO 2 uptakes and heats of adsorption at both low (0.15 bar) and high pressures (10 bar). For this large validation set, the SQE-MEPO ESP deviated from the QM ESP by 30% less than other parametrized charge determination methods with a mean absolute deviation (MAD) of 6.47 mHartree compared to the next closest method with a MAD of 9.53 mHartree. When comparing the CO 2 uptakes and heats of adsorption calculated with SQE-MEPO charges compared to charges best fit to reproduce the QM ESP, SQE-MEPO was found to have a have Pearson and Spearman correlation coefficients of >0.95 at both low and high pressures. SQE-MEPO allows for rapid charges to be generated for MOFs that provides DFT quality electrostatic interactions when simulating adsorption properties that are ideal for high throughput screening.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".