Beyond TD-DFT: Assessing the Bethe–Salpeter Equation within the GW Approximation for Absorption Properties
Bibliographic record
Abstract
Time-dependent density functional theory (TD-DFT) has been the “go-to” computational approach for predicting optical absorption properties, balancing computational efficiency and reasonable accuracy. However, the Bethe–Salpeter equation within the GW approximation (GW/BSE) is rapidly emerging as a powerful alternative to overcoming key limitations of TD-DFT. This letter presents a comprehensive evaluation of one-photon and two-photon absorption (1PA and 2PA) properties across a chemically diverse set of fluorophores, comparing TD-DFT with the eigenvalue self-consistent GW (evGW/BSE) and single-shot G 0 W 0 /BSE approaches, within the resolution-of-identity approximation, relative to resolution-of-identity second-order approximate coupled-cluster (RI-CC2). While TD-DFT maintains its dominance for 1PA, both evGW/BSE and G 0 W 0 /BSE exhibit superiority for 2PA, offering lower absolute errors and stronger agreement with qualitative trends. A persistent challenge in TD-DFT is the trade-off between quantitative accuracy and capturing structure–property trends, with no single functional reliably achieving both. In contrast, the tested GW/BSE approaches overcome this limitation, offering reliable values alongside a robust linear correlation. These findings further demonstrate evGW/BSE and G 0 W 0 /BSE as promising frameworks for modeling optical properties including 2PA.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".