Electronic circular dichroism spectra calculation based on generalized energy-based fragmentation approach
Bibliographic record
Abstract
Electronic circular dichroism (ECD) spectrum is an important tool for assessing molecular chirality. Traditional methods, like linear response time-dependent density functional theory (LR-TDDFT), predict ECD spectra well for small or medium-sized molecules, but struggle with large systems due to high computational costs, making it a significant challenge to accurately and efficiently predict the ECD properties of complex systems. Within the framework of the generalized energy-based fragmentation (GEBF) method for localized excited states (ESs) calculation, we propose a combination algorithm for calculating rotatory strengths of ESs in condensed phase systems. This algorithm estimates the rotatory strength of the total system by calculating and combining the transition electric and magnetic dipole moments of subsystems. We have used the GEBF method to calculate the ECD properties of chiral drug molecule derivatives, green fluorescent protein, and cyclodextrin derivatives, and compared their results with traditional methods or experimental data. The results show that this method can efficiently and accurately predict the ECD spectra of these systems. Thus, the GEBF method for ECD spectra demonstrates great potential in the chiral analysis of complex systems and chiral material design, promising to become a powerful theoretical tool in chiral chemistry.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".