Structural Evolution of Tcn (n = 4-20) Clusters from First-Principles Global Minimization.

Area of Science:

• Physical Chemistry
• Materials Science
• Computational Chemistry

Background:

• Understanding the structure of metal clusters is crucial for predicting their properties.
• Previous models for Technetium (Tcn) clusters may not represent their most stable configurations.

Purpose of the Study:

• To explore the structural evolution of Tcn clusters (n=4-20) using advanced computational methods.
• To identify novel, highly stable cluster structures and analyze their growth patterns.

Main Methods:

• Employed a first-principles global minimization technique: basin-hopping combined with density functional theory (BH-DFT) geometry optimization.
• Analyzed cluster growth sequences, binding energies per atom, relative stabilities, and magnetic moments as a function of cluster size.

Main Results:

• Discovered significantly more stable Tcn cluster structures compared to previous models.
• Identified specific "magic sizes" exhibiting enhanced stability and symmetry.
• Observed that Tc19 prefers an octahedral (Oh) symmetry, resembling a face-centered-cubic fragment, with unique corner atom reactivity.

Conclusions:

• BH-DFT is a powerful method for discovering new and stable cluster structures.
• The study elucidates the geometric shell formation and identifies key structural motifs in Tcn clusters.
• Tc19's unique structure suggests potential for specialized chemical reactivity.