tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes
Luca Cibinel, Trond Linjordet, Johan Pensar, David Balcells, Riccardo De Bin, Basil Ell

TL;DR
This paper introduces tmQM-RDF, a comprehensive knowledge graph dataset of around 50,000 transition metal complexes, designed to facilitate machine learning and computational analysis in chemistry.
Contribution
The paper presents a novel RDF-based knowledge graph dataset for TMCs, integrating detailed qualitative and quantitative data to support advanced analysis and manipulation tasks.
Findings
Effective representation of TMCs in RDF format
Promising results in TMC manipulation tasks using simple models
Rich dataset enabling machine learning applications in chemistry
Abstract
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 datasets, 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 modelling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Graph Neural Networks · Asymmetric Hydrogenation and Catalysis
