Related Experiment Video
Updated: Jun 25, 2026

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry
Published on: June 8, 2022
tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes
Luca Cibinel1,2, Trond Linjordet3,4, Johan Pensar1,2
1Integreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.
A new dataset, transition metal quantum mechanics RDF (tmQM-RDF), offers detailed descriptions of 60,000 transition metal complexes. This knowledge graph aids machine learning in chemistry, facilitating the study of these valuable compounds.
Area of Science:
- Chemistry
- Materials Science
- Computational Science
Background:
- Transition metal complexes (TMCs) are crucial in various chemical applications, including catalysis and medicinal chemistry.
- Studying TMCs requires comprehensive data for effective computational analysis and machine learning.
- Existing datasets may lack the detailed qualitative and quantitative information needed for advanced research.
Purpose of the Study:
- To introduce the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a novel knowledge graph for TMCs.
- To provide a user-friendly Python package (tmqmrdfdata) for accessing and utilizing the dataset.
- To demonstrate the utility of tmQM-RDF in TMC manipulation tasks using machine learning.
Main Methods:
- Constructed a knowledge graph using the Resource Description Framework (RDF) vocabulary.
- Collected and curated detailed descriptions for approximately 60,000 TMCs.
- Developed the tmqmrdfdata Python package for data access and manipulation.
Main Results:
- The tmQM-RDF dataset contains rich, detailed descriptions of ~60k TMCs, including compositional and molecular graph information.
- The tmqmrdfdata package offers a user-friendly interface to this extensive knowledge graph.
- Exploiting tmQM-RDF for TMC manipulation tasks showed promising performance with simple probabilistic models.
Conclusions:
- The tmQM-RDF dataset represents a significant contribution to data modeling for transition metal complexes.
- Accessible data and user-friendly tools like tmqmrdfdata can accelerate machine learning applications in TMC research.
- The dataset's rich information enables effective TMC analysis and manipulation, even with basic models.
More Related Videos
09:45Accessing Valuable Ligand Supports for Transition Metals: A Modified, Intermediate Scale Preparation of 1,2,3,4,5-Pentamethylcyclopentadiene
Published on: March 20, 2017
06:53Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
Published on: June 9, 2023
Related Concept Videos
Properties of Transition Metals
Metal-Ligand Bonds
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
Crystal Field Theory - Octahedral Complexes
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
Complexation Equilibria: The Chelate Effect
Properties of Organometallic Compounds
Ladder Diagrams: Complexation Equilibria
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...