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Metal-Ligand Bonds02:51

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The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
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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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Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides.

Pandu Wisesa1, Christopher M Andolina1, Wissam A Saidi1

  • 1Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, United States.

The Journal of Physical Chemistry Letters
|January 9, 2023
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Summary

Deep neural network potentials (DNPs) can now model diverse metal oxides, including various oxidation states, without needing extra charge data. This breakthrough enables accurate simulations for a wider range of materials.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid State Physics

Background:

  • Machine learning interatomic potentials using neural networks excel at modeling metallic systems.
  • Ionic systems, like metal oxides, present challenges due to their complex chemical nature and varying oxidation states, limiting current model applications.
  • Existing models often require specific composition and oxidation state information, hindering broader applicability.

Purpose of the Study:

  • To demonstrate that deep neural network potentials (DNPs) can accurately model various properties of metal oxides across different oxidation states.
  • To develop and validate DNPs for Ag₂O, CuO, MgO, PtO₂, and ZnO without imposing oxidation state limitations during training.
  • To explore methods for augmenting DNP databases to improve transferability for new material forms and properties.

Main Methods:

  • Creation and validation of deep neural network potentials (DNPs) for multiple metal oxides (Ag₂O, CuO, MgO, PtO₂, ZnO).
  • Training DNPs without predefined oxidation state constraints.
  • Augmenting the training database to enhance model transferability for polymorphs, surface energies, and thermal expansion.
  • Performing molecular dynamics simulations to assess stability, pressure/temperature interpolation, and thermal expansion accuracy.

Main Results:

  • Successfully created and validated DNPs for Ag₂O, CuO, MgO, PtO₂, and ZnO, capable of modeling diverse oxidation states.
  • Demonstrated that DNP transferability can be enhanced by augmenting the training database, improving predictions for new polymorphs and surface energies.
  • Validated the ability of DNPs to accurately interpolate across significant pressure and temperature ranges.
  • Confirmed the stability of DNPs over long molecular dynamics simulations and their capacity to replicate nonharmonic thermal expansion.

Conclusions:

  • Deep neural network potentials (DNPs) offer a powerful and versatile approach for modeling metal oxides, overcoming limitations of previous methods.
  • The developed DNPs accurately predict material properties across various oxidation states and conditions without requiring explicit charge information.
  • Database augmentation strategies significantly improve DNP transferability, enabling accurate predictions for unexplored material characteristics and conditions.
  • These findings pave the way for more efficient and accurate computational materials science research involving a wide range of oxide systems.