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Related Concept Videos

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory
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...
Properties of Transition Metals02:58

Properties of Transition Metals

Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
Valence Bond Theory02:42

Valence Bond Theory

Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
Colors and Magnetism03:02

Colors and Magnetism

Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human eye.
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Related Experiment Video

Updated: Jul 5, 2026

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal

Abdulmujeeb T Onawole1

  • 1Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia.

Journal of Chemical Information and Modeling
|July 3, 2026
PubMed
Summary

We developed tmGNN-XAI, a new AI tool that predicts electronic properties for transition metal complexes using only molecular structure. It provides atom-level explanations and reliability assessments, aiding in materials discovery.

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Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry
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Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

Related Experiment Videos

Last Updated: Jul 5, 2026

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks
06:53

Magnetometric Characterization of Intermediates in the Solid-State Electrochemistry of Redox-Active Metal-Organic Frameworks

Published on: June 9, 2023

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry
16:11

Thermochemical Studies of Ni(II) and Zn(II) Ternary Complexes Using Ion Mobility-Mass Spectrometry

Published on: June 8, 2022

Area of Science:

  • Computational chemistry
  • Materials science
  • Artificial intelligence

Background:

  • Predicting electronic properties of transition metal complexes (TMCs) from 2D molecular graphs is difficult.
  • Existing models lack transferability or require 3D data.
  • Tools for holistic prediction with explainability and uncertainty are limited.

Purpose of the Study:

  • Introduce tmGNN-XAI, a multitask relational graph convolutional network.
  • Enable direct prediction of quantum-chemical properties from SMILES strings.
  • Provide perturbation-based atom-level attributions for enhanced interpretability.

Main Methods:

  • Developed a graph convolutional network (GCN) model, tmGNN-XAI.
  • Encoded dative coordination bonds as a distinct edge type.
  • Trained on 100,703 TMCs from the tmQM dataset, spanning 30 transition metals.

Main Results:

  • Achieved competitive performance against baselines (e.g., R²=0.979 for metal partial charge, R²=0.964 for HOMO).
  • Identified donor atoms (N, O, S, P) as highly important across properties (>99.8% of complexes).
  • Demonstrated a trust framework improving prediction accuracy for confident results (1.6-2.5x lower MAE).

Conclusions:

  • tmGNN-XAI offers accurate and explainable electronic property prediction for TMCs.
  • The trust framework enhances prediction reliability and guides decision-making.
  • The tool generalizes to DFT validation, screening, and redox prediction, serving as a first-tier screening tool.