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We used AI and DFTB3 to study zinc-doped MgO nanoparticles. Zinc stabilizes the nanoparticles, narrows the energy gap, and enhances electron acceptance, creating an optimal operating window for devices.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Ultrasmall nanoparticles require precise control over size and composition.
  • Understanding dopant effects in metal oxide nanoparticles is crucial for advanced applications.

Purpose of the Study:

  • To map the size-composition landscape of Zn-doped MgO nanoparticles.
  • To investigate the electronic and thermodynamic properties of these nanoparticles.
  • To develop and apply a physics-guided AI workflow for materials discovery.

Main Methods:

  • Coupling density-functional tight-binding (DFTB3) with a multimodal AI workflow.
  • Investigating nanoparticles with radii of 0.8 and 0.9 nm and Zn content from 0 to 25 at%.
  • Analyzing density of states (DOS), geometry, and image features using AI.

Main Results:

  • Zn incorporation stabilizes MgO nanoparticles and narrows the energy gap.
  • Electronic properties like HOMO-LUMO levels, ionization potential, and electron affinity are tuned by Zn content.
  • An optimal operating window of 10-15% Zn was identified for balancing electron acceptance and robustness.
  • The AI model accurately corrected DFTB3 trends under data scarcity.

Conclusions:

  • Zn-doped MgO nanoparticles offer tunable electronic properties for energy applications.
  • The AI-driven approach accelerates materials discovery for ultrasmall nanoparticles.
  • These findings support applications in Li-S batteries and oxide-based electronic devices.