Engineered MgO Nanoparticles with Tunable Electronic Signatures for Energy Applications

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.