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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".