Abstract
Halide perovskites have emerged as one of the most interesting materials for optoelectronic applications due to their favorable properties, such as defect tolerance and long charge carrier lifetimes, which are attributed to their dynamic softness. However, this softness has led to apparent disagreements between the local instantaneous and global average structures of these materials. In this study, we rationalize this situation through an assessment of the local tilt angles of octahedra in the perovskite structure using large-scale molecular dynamics simulations based on machine-learned potentials trained using density functional theory calculations. We compare structural properties given by different density functionals [local density approximation, PBE, PBE + D3, PBEsol, strongly constrained and appropriately normed (SCAN), SCAN + rVV10, and van der Waals density functional with consistent exchange] and establish trends across a family of CsMX3 perovskites with M = Sn or Pb and X = Cl, Br or I. Notably, we demonstrate strong short-range ordering in the cubic phase of halide perovskites. This ordering is reminiscent of the tetragonal phase and provides the bridge between the disordered local structure and the global cubic arrangement. Our results provide a deeper understanding of the structural properties of halide perovskites and their local distortions, which is crucial for further understanding their optoelectronic properties.
| Original language | English |
|---|---|
| Pages (from-to) | 6737–6744 |
| Number of pages | 8 |
| Journal | Chemistry of Materials |
| Volume | 35 |
| Issue number | 17 |
| Early online date | 21 Aug 2023 |
| DOIs | |
| Publication status | Published - 12 Sept 2023 |
Bibliographical note
Acknowledgments:This work was funded by the Swedish Research Council (grant numbers 2018-06482, 2019-03993, 2020-04935, 2021-05072), the Area of Advance Nano at Chalmers, and the Chalmers Initiative for Advancement of Neutron and Synchrotron Techniques. J.W. acknowledges the Swedish Strategic Research Foundation through a Future Research Leader programme (FFL21-0129). The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at C3SE, NSC, HPC2N, and PDC partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973.
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