Evaluation of tight-binding DFT performance for the description of organic photochromes properties
Bibliographic record
Abstract
Photochromic molecules are widely studied and developed for their many potential applications. To optimize the required properties through theoretical models, a considerable chemical space is to be explored, and their environment in devices is to be accounted for.. To this end, cheap and reliable computational methods can be powerful tools to steer synthetic developments. As ab initio methods remain costly for extensive studies (in terms of the size of the system and/or number of molecules), semiempirical methods such as density functional tight-binding (TB) could offer a good compromise between accuracy computational cost. However, these approaches necessitate benchmarking on the families of compounds of interest. Thus, the aim of the present study is to evaluate the accuracy of several key features calculated with TB methods (DFTB2, DFTB3, GFN2-xTB, and LC-DFTB2) for three sets of photochromic organic molecules: azobenzene (AZO), norbornadiene/quadricyclane (NBD/QC), and dithienylethene (DTE) derivatives. The features considered here are the optimized geometries, the difference in energy between the two isomers (ΔE), and of the energies of the first relevant excited states. All the TB results are compared to those obtained with DFT methods and state-of-the-art electronic structure calculation methods: DLPNO-CCSD(T) for ground states and DLPNO-STEOM-CCSD for excited states. Our results show that, overall, DFTB3 is the TB method leading to the best results for the geometries and the ΔE values and can be used alone for these purposes for NBD/QC and DTE derivatives. Single point calculations at the r2SCAN-3c level using TB geometries allow circumventing the deficiencies of the TB methods in the AZO series. For electronic transition calculations, the range separated LC-DFTB2 method is the most accurate TB method tested for AZO and NBD/QC derivatives, in close agreement with the reference.
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 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".