Determining the chemical ordering in nanoalloys by considering atomic coordination types

Area of Science:

• Computational chemistry
• Materials science
• Nanotechnology

Background:

• Determining the most stable chemical arrangement in bimetallic nanoparticles is crucial for their properties.
• Surrogate energy models, using structural descriptors, are essential for approximating nanoalloy energies in computational studies.

Purpose of the Study:

• To systematically assess the performance of data-efficient topological descriptors for predicting bimetallic nanoalloy energies.
• To introduce and evaluate a novel descriptor based on atomic coordination types for enhanced accuracy and interpretability.
• To demonstrate the application of these descriptors in predicting nanoalloy stability and reactivity for catalysis.

Main Methods:

• Utilized global optimization algorithms and surrogate energy models.
• Evaluated existing topological descriptors and introduced a new coordination-based descriptor.
• Employed a basin hopping algorithm to generate convex hulls for PdZn nanoalloys.
• Integrated machine-learning adsorption models for electrocatalysis studies.

Main Results:

• Topological descriptors effectively predict energies of metal nanoalloys with varying chemical orderings.
• The new coordination-based descriptor enhances general accuracy and quantifies inner nanoparticle orderings.
• Convex hulls and active surface site distributions for PdZn nanoalloys were successfully generated.
• The combined approach enables rapid evaluation of nanoalloy reactivity landscapes.

Conclusions:

• Topological and coordination-based descriptors are powerful tools for understanding bimetallic nanoalloy energetics and structure.
• These computational methods accelerate the discovery and design of advanced nanomaterials for catalysis.
• The integration with machine learning offers a pathway for efficient screening of catalytic materials.