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Explores transition metal complex properties in chemistry, highlighting machine learning's role.
High Resolution Image Download MS PowerPoint Slide Transition metal complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible data sets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modeling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) data set, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 60k TMCs. These descriptions are both qualitative and quantitative, encompassing the compositional nature of TMCs as well as the entirety of their molecular graphs. The Python package tmqmrdfdata is released together with the knowledge graph to provide a user-friendly interface. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.
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Cibinel et al. (2026) studied this question.
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