Computational Design of Nanoclusters by Property-Based Genetic Algorithms: Tuning the Electronic Properties of (TiO$_2$)$_n$ Clusters

TL;DR

This paper develops genetic algorithms to design TiO2 nanoclusters with targeted electronic properties, revealing key structural features influencing their catalytic potential and property correlations.

Contribution

It introduces property-based genetic algorithms for nanocluster design, linking structural features to electronic properties in TiO2 clusters up to size 20.

Findings

Dangling-O atoms influence VEA and VIP

Electronic properties correlate more with structure than size

Structural features serve as potential catalytic sites

Abstract

In order to design clusters with desired properties, we have implemented a suite of genetic algorithms tailored to optimize for low total energy, high vertical electron affinity (VEA), and low vertical ionization potential (VIP). Applied to (TiO$_2$)$_n$ clusters, the property-based optimization reveals the underlying structure-property relations and the structural features that may serve as active sites for catalysis. High VEA and low VIP are correlated with the presence of several dangling-O atoms and their proximity, respectively. We show that the electronic properties of (TiO$_2$)$_n$ up to n=20 correlate more strongly with the presence of these structural features than with size.