Efficient Computation of Metal Halide Perovskites Properties using the Extended Density Functional Tight Binding: GFN1-xTB Method

TL;DR

This paper evaluates the GFN1-xTB method as a computationally efficient alternative to DFT for simulating the properties of metal halide perovskites, showing promising accuracy and potential for further refinement.

Contribution

It demonstrates the applicability of GFN1-xTB for MHPs, benchmarking its performance against experiments and DFT, and discusses its tunability for improved accuracy.

Findings

GFN1-xTB provides accurate structural and vibrational predictions for MHPs.

The method shows comparable results to DFT and experiments for many properties.

Some limitations are identified in specific geometries and compositions.

Abstract

In recent years, metal halide perovskites (MHPs) for optoelectronic applications have attracted the attention of the scientific community due to their outstanding performance. The fundamental understanding of their physicochemical features is essential for improving their efficiency and stability. Atomistic and molecular simulations have played an essential role in the description of the optoelectronic properties and dynamical behaviour of MHPs, respectively. However, the complex interplay of the dynamical and optoelectronic properties in MHPs requires the simultaneous modelling of electrons and ions in relatively large systems, which entails a high computational cost, sometimes not affordable by the standard quantum mechanics methods, such as Density Functional Theory (DFT). Here, we explore the suitability of the recently developed Density Functional Tight Binding (DFTB) method, GFN1-xTB, for simulating MHPs with the aim of exploring an efficient alternative to DFT. The performance of GFN1-xTB for computing structural, vibrational and optoelectronic properties of several MHPs is benchmarked against experiments and DFT calculations. In general, this method produces accurate predictions for many of the properties of the studied MHPs, which are comparable to DFT and experiments. However, we also identify a few shortcomings, related to specific geometries and chemical compositions. Nevertheless, we believe that the tunability of GFN1-xTB is the key to resolving any observed issues and we propose specific targets, whose refinement will turn this method into a powerful computational tool for the study of MHPs and beyond.