Cluster-Based Approach Utilizing Optimally Tuned TD-DFT to Calculate Absorption Spectra of Organic Semiconductor Thin Films
Bibliographic record
Abstract
The photophysics of organic semiconductor (OSC) thin films or crystals has garnered significant attention in recent years since a comprehensive theoretical understanding of the various processes occurring upon photoexcitation is crucial for assessing the efficiency of OSC materials. To date, research in this area has relied on methods using Frenkel-Holstein Hamiltonians, calculations of the GW-Bethe-Salpeter equation with periodic boundaries, or cluster-based approaches using quantum chemical methods, with each of the three approaches having distinct advantages and disadvantages. In this work, we introduce an optimally tuned, range-separated time-dependent density functional theory approach to accurately reproduce the total and polarization-resolved absorption spectra of pentacene, tetracene, and perylene thin films, all representative OSC materials. Our approach achieves excellent agreement with experimental data (mostly ≤0.1 eV) when combined with the utilization of clusters comprising multiple monomers and a standard polarizable continuum model to simulate the thin-film environment. Our protocol therefore addresses a major drawback of cluster-based approaches and makes them attractive tools for OSC investigations. Its key advantages include its independence from external, system-specific fitting parameters and its straightforward application with well-known quantum chemical program codes. It demonstrates how chemical intuition can help to reduce computational cost and still arrive at chemically meaningful and almost quantitative results.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".