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

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.

4 figs, 5 pages

References in corpus (3)

• Stability and metastability of clusters in a reactive atmosphere: theoretical evidence for unexpected stoichiometries of MgMOx
• Genetic Design of Enhanced Valley Splitting towards a Spin Qubit in Silicon
• Efficient ab initio schemes for finding thermodynamically stable and metastable atomic structures: Benchmark of cascade genetic algorithms

Cited by in corpus (5)

• Uncovering structure-property relationships of materials by subgroup discovery
• GAtor: A First Principles Genetic Algorithm for Molecular Crystal Structure Prediction
• Theoretical Insights into C$-$H Bond Activation of Methane by Transition Metal Clusters: The Role of Anharmonic Effects
• Structure and electronic properties of transition-metal/Mg bimetallic clusters at realistic temperatures and oxygen partial pressures
• Metastability Triggered Reactivity in Clusters at Realistic Conditions: A Case Study of N-doped (TiO${}_2$)${}_n$ for Photocatalysis