Using electronic polarization from the internal continuum (EPIC) for intermolecular interactions
Bibliographic record
Abstract
Recently, the vacuum-phase molecular polarizability tensor of various molecules has been accurately modeled (Truchon et al., J Chem Theory Comput 2008, 4, 1480) with an intramolecular continuum dielectric model. This preliminary study showed that electronic polarization can be accurately modeled when combined with appropriate dielectric constants and atomic radii. In this article, using the parameters developed to reproduce ab initio quantum mechanical (QM) molecular polarizability tensors, we extend the application of the "electronic polarization from internal continuu" (EPIC) approach to intermolecular interactions. We first derive a dielectric-adapted least-square-fit procedure similar to RESP, called DRESP, to generate atomic partial charges based on a fit to a QM abinitio electrostatic potential (ESP). We also outline a procedure to adapt any existing charge model to EPIC. The ability of this to reproduce local polarization, as opposed to uniform polarization, is also examined leading to an induced ESP relative root mean square deviation of 1%, relative to ab initio, when averaged over 37 molecules including aromatics and alkanes. The advantage of using a continuum model as opposed to an atom-centered polarizable potential is illustrated with a symmetrically perturbed atom and benzene. We apply EPIC to a cation-pi binding system formed by an atomic cation and benzene and show that the EPIC approach can accurately account for the induction energy. Finally, this article shows that the ab initio electrostatic component in the difficult case of the H-bonded 4-pyridone dimer, a highly polar and polarized interaction, is well reproduced without adjusting the vacuum-phase parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".