An Ontology for Theoretical Chemistry

Ontologies, as formal and structured representations of knowledge within a domain, have been instrumental in enabling data integration, knowledge sharing, and interoperability. For instance in chemistry experimental techniques, instrumentation, and chemical entities are codified in ontologies such as the Chemical Methods Ontology[1] and the Chemical Information Ontology[2]. Surprisingly, no comparably comprehensive ontology exists for theoretical chemistry. An ontology is the foundation of the necessary standardization for building reusable software libraries, workflow engines, and data repositories. Classes in software code could be directly derived from the ontology and therefore facilitate the modular construction of software components. Thereby greatly enhancing the efficiency and reproducibility of computational workflows. Creating this ontology, however, is intrinsically a community endeavour. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem[3] or MolSSI QCSchema[4]). In the process of building the “comprehensive ontology” we should align with these existing vocabularies to maximise interoperability. For community inclusion an iterative, open development on collaborative platforms (e.g., GitHub) employing continuous integration should be enfostered. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. [1] https://github.com/rsc-ontologies/rsc-cmo [2] Hastings J., Chepelev L., Willighagen E., Adams N., Steinbeck C., Dumontier M., PLoS One, 6(10):e25513 (2011) [3] Krdzavac N., Mosbach S., Nurkowski D., Buerger P., Akroyd J., Martin J., Menon A., Kraft M., J Chem Inf Model., 22;59(7):3154-3165 (2019) [4] https://molssi.org/software/qcschema-2/ & https://github.com/MolSSI/QCSchema