Comparative analysis of GFN methods in geometry optimization of small organic semiconductor molecules: A DFT benchmarking study

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.