tmQM-RDF数据集：一种表示过渡金属配合物的知识图谱
tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes
AI总结：
tmQM-RDF数据集通过构建知识图谱，为过渡金属配合物的研究提供丰富的定量和定性信息，支持高效的分析和处理任务。
摘要：
过渡金属配合物（TMCs）在化学中具有广泛的实际应用，从催化到药物化学都有可能的应用。因此，研究TMCs及其性质是一个充满潜力的领域，在此领域中，机器学习和计算方法可以提供实质性的帮助。为此，需要适当且易于获取的数据集，收集广泛的信息，以促进此类化合物的有效分析和研究。本文通过引入过渡金属量子力学RDF（tmQM-RDF）数据集，一种使用资源描述框架（RDF）词汇构建的知识图谱，贡献于数据建模的努力。该数据集收集了约5万个TMCs的丰富和详细描述。这些描述既定性又定量，涵盖TMCs的组成性质，包括其构成配体的性质，以及它们的整个分子图。所提出的表示方法的一个例子被展示出来，展示了tmQM-RDF中可用信息如何被用于TMC操作任务，即使使用相对简单的概率模型也能取得有希望的性能。
英文摘要：
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 compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
