LCAO-TDDFT- <i>k</i> - <b> <i>ω</i> </b> : spectroscopy in the optical limit
Bibliographic record
Abstract
Abstract Understanding, optimizing, and controlling the optical absorption process, exciton gemination, and electron–hole separation and conduction in low dimensional systems is a fundamental problem in materials science. However, robust and efficient methods capable of modelling the optical absorbance of low dimensional macromolecular systems and providing physical insight into the processes involved have remained elusive. We employ a highly efficient linear combination of atomic orbitals (LCAOs) representation of the Kohn–Sham (KS) orbitals within time dependent density functional theory (TDDFT) in the reciprocal space ( k ) and frequency ( ω ) domains, as implemented within our LCAO-TDDFT- k - ω code, applying either a priori or a posteriori the derivative discontinuity correction of the exchange functional Δ x to the KS eigenenergies as a scissors operator. In so doing we are able to provide a semi-quantitative description of the photoabsorption cross section, conductivity, and dielectric function for prototypical 0D, 1D, 2D, and 3D systems within the optical limit (‖ q ‖ → 0 + ) as compared to both available measurements and from solving the Bethe–Salpeter equation with quasiparticle G 0 W 0 eigenvalues ( G 0 W 0 -BSE). Specifically, we consider 0D fullerene (C 60 ), 1D metallic (10, 0) and semiconducting (10, 10) single-walled carbon nanotubes, 2D graphene ( Gr ) and phosphorene ( Pn ), and 3D rutile (R-TiO 2 ) and anatase (A-TiO 2 ). For each system, we also employ the spatially and energetically resolved electron–hole spectral density to provide direct physical insight into the nature of their optical excitations. These results demonstrate the reliability, applicability, efficiency, and robustness of our LCAO-TDDFT- k - ω code, and open the pathway to the computational design of macromolecular systems for optoelectronic, photovoltaic, and photocatalytic applications in silico .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".