Exploring Conformer Search Workflows Using OpenBabel, Hierarchical Clustering, and Quantum Chemical Optimizations: A Workflow Study Including Oligothiophenes and Y6

Identifying all energetically relevant conformers is crucial for accurate quantum chemical predictions of molecular and material properties, yet assessing the completeness of a conformer ensemble remains a persistent challenge. Among these is the opensource 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 for a broad set of reference molecules. We demonstrate our workflow for Oligothiophenes with their well-known conformational landscapes. 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. We demonstrate the transferability of our workflow by applying it to the more complex and technologically important nonfullerene acceptor molecule Y6. By combining RMSD-based genetic searches with hierarchical clustering and normalized RMSD metrics, we establish a tree-wise, iterative conformer search strategy in which secondary searches are initiated from geometrically distinct conformers identified in preceding generations. We present a thorough benchmark and workflow analysis of a tree-wise genetic conformer search on a benchmark set of small molecules, whose conformers are well-established in the literature. This work provides a practical, benchmark-based framework for selecting suitable OpenBabel parameters and developing reliable workflows for conformer identification through hierarchical clustering and DFT-based refinement.