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Record W4413227743 · doi:10.26434/chemrxiv-2025-f7nwt

Beyond TD-DFT: Assessing the Bethe-Salpeter Equation within the GW Approximation for Absorption Properties

2025· preprint· en· W4413227743 on OpenAlexaff
Arthur Henderson, Ismael A. Elayan, Alex Brown

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldEngineering
TopicNonlinear Optical Materials Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBethe–Salpeter equationDensity functional theoryAbsorption (acoustics)PhotonStatistical physicsDominance (genetics)Two-photon absorptionComputational physicsComputer scienceBiological systemPhysicsChemistryComputational chemistryQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

Time-dependent density functional theory (TD-DFT) has been the “go-to” source 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, evaluating TD-DFT and GW/BSE relative to resolution-of-identity second-order approximate coupled-cluster (RI-CC2). While TD-DFT maintains its dominance for 1PA, GW/BSE exhibits superiority in 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, GW/BSE overcomes this limitation, offering reliable values alongside robust linear correlation. These findings establish GW/BSE as a promising framework for modeling optical properties beyond TD-DFT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.275
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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