(Towards) An Ontology for Theoretical Chemistry

Presentation given at the Ontologies4Chem Workshop 2025. Abstract:Surprisingly, no comprehensive ontology exists for theoretical chemistry. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem or MolSSI QCSchema). In the process of building a comprehensive ontology for theoretical chemistry we should align with these existing vocabularies to maximise interoperability. 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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-11
DOI
https://doi.org/10.5281/zenodo.18339269
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

(Towards) An Ontology for Theoretical Chemistry

Mario Wolter
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

(Towards) An Ontology for Theoretical Chemistry

Mario Wolter
article en

Abstract

Presentation given at the Ontologies4Chem Workshop 2025. Abstract:Surprisingly, no comprehensive ontology exists for theoretical chemistry. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem or MolSSI QCSchema). In the process of building a comprehensive ontology for theoretical chemistry we should align with these existing vocabularies to maximise interoperability. 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.

Zenodo (CERN European Organization for Nuclear Research)
Technische Universität Braunschweig (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Machine Learning in Materials Science
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