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Related Concept Videos

Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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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...
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Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

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Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
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Valence Bond Theory02:42

Valence Bond Theory

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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...
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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...
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Thermodynamic Potentials01:26

Thermodynamic Potentials

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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Properties of Transition Metals02:58

Properties of Transition Metals

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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.
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Artificial Neural Network-Based Density Functional Approach for Adiabatic Energy Differences in Transition Metal

João Paulo Almeida de Mendonça1, Lorenzo Antonio Mariano1, Emilie Devijver2

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|October 16, 2023
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Summary

A new machine learning approach enhances density functional theory for predicting spin state energetics in transition metal complexes, improving accuracy for computational chemistry applications.

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Area of Science:

  • Computational chemistry and physics
  • Quantum chemistry
  • Materials science

Background:

  • Approximate Kohn-Sham density functional theory (DFT) is widely used but struggles with spin state energetics.
  • Accurate prediction of spin states is crucial for understanding transition metal complex behavior.

Purpose of the Study:

  • To develop an enhanced exchange-correlation functional for improved prediction of adiabatic energy differences in transition metal complexes.
  • To leverage machine learning to correct deficiencies in existing DFT functionals.

Main Methods:

  • Developed a machine learning approach using an artificial neural network correction.
  • Trained the correction on a small dataset of electronic densities, atomization energies, and spin state energetics.
  • Employed a bioinspired particle swarm optimization for training the functional.

Main Results:

  • The machine-learned meta-generalized gradient approximation (mGGA) functional significantly outperforms existing DFT functionals.
  • Demonstrated superior accuracy in predicting adiabatic energy differences for diverse transition metal complexes.
  • Showcased the effectiveness of the mGGA functional across various coordination environments and metal types.

Conclusions:

  • The developed machine learning-enhanced DFT functional offers a promising advancement for accurate spin state energetics prediction.
  • This approach provides a more reliable tool for computational studies involving transition metal complexes.
  • Highlights the potential of integrating machine learning with DFT for complex chemical problems.