Efficient modelling of ion structure and dynamics in inorganic metal halide perovskites
Metal halide perovskites (MHPs) are nowadays one of the most studied semiconductors due to their exceptional performance as active layers in solar cells. Although MHPs are excellent solid-state semiconductors, they are also ionic compounds, where ion migration plays a decisive role in their formation, their photovoltaic performance and their long-term stability. Given the above-mentioned complexity, molecular dynamics simulations based on classical force fields are especially suited to study MHP properties, such as lattice dynamics and ion migration. In particular, the possibility to model mixed compositions is important since they are the most relevant to optimize the optical band gap and the stability. With this intention, we employ DFT calculations and a genetic algorithm to develop a fully transferable classical force field valid for the benchmark inorganic perovskite compositional set CsPb(BrxI1−x)3 (x = 0, 1/3, 2/3, 1). The resulting force field reproduces correctly, with a common set of parameters valid for all compositions, the experimental lattice parameter as a function of bromide/iodide ratio, the ion–ion distances and the XRD spectra of the pure and mixed structures. The simulated elastic constants, thermal conductivities and ion migration activation energies of the pure compounds are also in good agreement with experimental trends. Our molecular dynamics simulations make it possible to predict the compositional dependence of the ionic diffusion coefficient on bromide/iodide ratio and vacancy concentration. Interestingly, compared to the pure compounds, we found a significantly lower activation energy for vacancy migration and faster diffusion for the mixed perovskites. This anomalous effect helps to understand the photoinduced phase segregation observed in the mixed perovskite. The method presented here represents a first step towards the generation of fully generic classical force fields of pure and mixed photovoltaic perovskites using genetic algorithms that optimize the required parameters for a wide range of lattice deformations.