Efficient Conformer Identification with OpenBabel: A Benchmark Workflow Using Oligothiophenes and Y6

Identifying all energetically relevant conformers is crucial for accurate quantum chemical predictions of molecular and material properties. To address this, a variety of powerful tools and methods have been developed for efficiently exploring often vast conformational spaces. Among these is the open-source toolkit OpenBabel, which offers multiple conformer generation algorithms and user-adjustable parameters that can significantly affect search outcomes. In this study, we systematically investigate how different methods and parameter choices in OpenBabel influence the conformer search results, using oligothiophenes as model systems. Oligothiophenes are ideal reference compounds due to their well-characterized conformational landscapes and scalability to larger systems. Our workflow involves: (i) generating conformers with OpenBabel, (ii) clustering them based on geometric similarity, and (iii) optimizing the most energetically favorable representatives of each cluster using density functional theory (DFT). We further explore whether additional conformational space can be accessed by reinitiating OpenBabel searches from DFT-relaxed structures, thereby possibly identifying previously overlooked but energetically relevant conformers. Finally, we demonstrate the transferability of our workflow by applying it to the more complex and technologically important non-fullerene acceptor molecule Y6. This work provides a robust framework for selecting suitable OpenBabel parameters and developing reliable workflows for conformer identification through hierarchical clustering and DFT-based refinement.