Comparative analysis of GFN methods in geometry optimization of small organic semiconductor molecules: A DFT benchmarking study
Bibliographic record
Abstract
This study benchmarks the GFN family of semi-empirical meth- ods (GFN1- x TB, GFN2- x TB, GFN0- x TB, and GFN-FF) against density functional theory (DFT) for the evaluation of opti- mized molecular geometries and electronic properties of small organic semiconductor molecules. This work offers a sys- tematic assessment of these computationally efficient quan- tum chemical methods and their accuracy-cost profiles when applied to a challenging class of systems, characterized, for instance, by extended π -conjugation, conformational flexi- bility, and sensitivity of properties to subtle structural changes. Two datasets are evaluated: a QM9-derived subset of small organic molecules and the Harvard Clean Energy Project (CEP) database of extended π -systems relevant to organic photo- voltaics. Structural agreement is quantified using heavy-atom RMSD, equilibrium rotational constants, bond lengths, and angles, while electronic property prediction is assessed via HOMO–LUMO energy gaps. Computational efficiency is as- sessed via CPU time and scaling behavior. GFN1- x TBand GFN2- x TBdemonstrate the highest structural fidelity, while GFN-FFoffers an optimal balance between accuracy and speed, particularly for larger systems. The results indicate that GFN- based methods are suitable for high-throughput molecular screening of small organic semiconductors, with the choice of method depending on accuracy-cost trade-offs. The find- ings support the deployment of GFN approaches in compu- tational pipelines for the discovery of organic electronics and materials, providing information on their strengths and limi- tations relative to established DFT methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.000 |
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 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".