Density‐Based Description of Molecular Polarizability for Complex Systems
Bibliographic record
Abstract
In this chapter, we have introduced a different approach to polarizability prediction in complex systems, which does not need solving the laborious coupled-perturbed Hartree–Fock (CPHF) or Kohn–Sham (CPKS) equations. To make that happen, some simple density-based functions from the information-theoretic approach (ITA) are employed. We have verified that strong linear regression correlations exist between the polarizability and ITA quantities for various complex systems of both localized and delocalized electronic structures. Illustrative applications include predicting the polarizability of complex proteins and excited-state systems. Intriguingly, combined with the linear-scaling generalized energy-based fragmentation (GEBF) method, one can predict the subsystem (of a few atoms or groups) polarizability based on the established linear regression equations, thus the total polarizability via a linear combination of subsystem polarizabilities for proteins. Overall, our computational results showcase that the GEBF-ITA protocol should be a robust and cost-effective theoretical tool in predicting molecular polarizabilities, especially when more advanced electronic structure methods are adopted.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".