Combining classical molecular dynamics and quantum mechanical methods for the description of electronic excitations: The case of carotenoids
Bibliographic record
Abstract
Carotenoids are important actors both in light-harvesting (LH) and in photoprotection functions of photosynthetic pigment-protein complexes. A deep theoretical investigation of this multiple role is still missing owing to the difficulty of describing the delicate interplay between electronic and nuclear degrees of freedom. A possible strategy is to combine accurate quantum mechanical (QM) methods with classical molecular dynamics. To do this, however, accurate force-fields (FF) are necessary. This article presents a new FF for the different carotenoids present in LH complexes of plants. The results show that all the important structural properties described by the new FF are in very good agreement with QM reference values. This increased accuracy in the simulation of the structural fluctuations is also reflected in the description of excited states. Both the energy order and the different nature of the lowest singlet states are preserved during the dynamics when the new FF is used, whereas an unphysical mixing is found when a standard FF is used.
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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 | 0.002 |
| 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 | 0.001 | 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".