Optical excitations of chlorophyll <i>a</i> and <i>b</i> monomers and dimers
Bibliographic record
Abstract
A necessary first step in the development of technologies such as artificial photosynthesis is understanding the photoexcitation process within the basic building blocks of naturally occurring light harvesting complexes (LHCs). The most important of these building blocks in biological LHCs such as LHC II from green plants are the chlorophyll a (Chl a) and chlorophyll b (Chl b) chromophores dispersed throughout the protein matrix. However, efforts to describe such systems are still hampered by the lack of computationally efficient and accurate methods that are able to describe optical absorption in large biomolecules. In this work, we employ a highly efficient linear combination of atomic orbitals (LCAOs) to represent the Kohn–Sham (KS) wave functions at the density functional theory (DFT) level and perform time-dependent density functional theory (TDDFT) calculations in either the reciprocal space and frequency domain (LCAO-TDDFT-k-ω) or real space and time domain (LCAO-TDDFT-r-t) of the optical absorption spectra of Chl a and b monomers and dimers. We find that our LCAO-TDDFT-k-ω and LCAO-TDDFT-r-t calculations reproduce results obtained with a plane-wave (PW) representation of the KS wave functions (PW-TDDFT-k-ω) but with a significant reduction in computational effort. Moreover, by applying the Gritsenko, van Leeuwen, van Lenthe, and Baerends solid and correlation derivative discontinuity correction Δx to the KS eigenenergies, with both LCAO-TDDFT-k-ω and LCAO-TDDFT-r-t methods, we are able to semiquantitatively reproduce the experimentally measured photoinduced dissociation results. This work opens the path to first principles calculations of optical excitations in macromolecular systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".