Integrated Continuum Dielectric Approaches To Treat Molecular Polarizability and the Condensed Phase: Refractive Index and Implicit Solvation
Bibliographic record
Abstract
The idea of using a dielectric continuum inside a molecule to accurately model molecular polarizability is extended to include a larger spectrum of bioorganic molecules and the condensed phase. Atomic polarization radii and an internal dielectric (ε in ) were fitted to reproduce ab initio B3LYP/aug-cc-pVTZ polarizability tensors taken from a data set of 707 molecules. The average unsigned error on the isotropic polarizability and anisotropy are 2.6% and 5.2%, respectively. It is shown that usual Poisson−Boltzmann contact radii and a low internal dielectric are not appropriate and require major revision. To account for the anisotropy of polarizability, the internal dielectric (ε in ) constant needs to be larger than 6.0. Reinterpreting the theoretical link between ε in and the experimental refractive index ( n ), this study shows, with a set of 23 organic molecules spanning the entire range of n, that even with ε in = 24 the obtained refractive indices can correlate well with experiment (slope of 1.00, intercept of 0.05, and R = 0.95). The novel methodology used here to calculate a macroscopic-like refractive index shows that the application of the EPIC parametrization to condensed phase leads to suitable behavior. Although the primary goal in developing EPIC was to include polarizability in explicit solvent calculations, we also extend the model to work with implicit solvent. This requires the use of a 3-zone smooth dielectric function to transition from the polarization dielectric inside the molecules to the dielectric continuum of the solvent. The parametrization and validation of this model are performed against 485 experimental free energies of hydration. Using 8 solvent cavity atomic radii and a single surface tension an average unsigned error of 1.1 kal/mol and a correlation coefficient of 0.9 are obtained, validating the use of the EPIC model in the condensed phase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".