An efficient way to model complex magnetite: assessment of SCC-DFTB against DFT

TL;DR

This paper evaluates the SCC-DFTB method for modeling magnetite, demonstrating it offers a computationally efficient alternative to DFT with comparable accuracy, thus enabling large-scale nanomaterial simulations for biomedical applications.

Contribution

The study introduces and validates SCC-DFTB with new parameters as a fast, accurate alternative to DFT for magnetite modeling, especially for large nanostructures.

Findings

SCC-DFTB with Coulomb correction closely matches DFT+U results.

SCC-DFTB significantly reduces computational cost compared to DFT.

Validated SCC-DFTB as a reliable tool for magnetite nanostructure simulations.

Abstract

Magnetite has attracted increasing attention in recent years due to its promising and diverse applications in biomedicine. Theoretical modelling can play an important role in understanding magnetite-based nanomaterials at the atomic scale for a deeper insight into the experimental observations. However, calculations based on density functional theory (DFT) are too costly for realistically large models of magnetite nanoparticles. Classical force field methods are very fast but lack of precision and of the description of electronic effects. Therefore, a cheap and efficient quantum mechanical simulation method with comparable accuracy than DFT is highly desired. Here, a less computational demanding DFT-based method, i.e. self-consistent charge density functional tight-binding (SCC-DFTB), is adopted to investigate magnetite bulk and low-index (001) surface with newly proposed parameters for Fe-O interactions. We report that SCC-DFTB with on-site Coulomb correction provides results in quantitatively comparable agreement with those obtained by DFT+U and hybrid functional methods. Therefore, SCC-DFTB is valued as an efficient and reliable method for the description magnetite. This assessment will promote SCC-DFTB computational studies on magnetite-based nanostructures that attract increasing attention for medical applications.