Phase Transitions in Inorganic Halide Perovskites from Machine-Learned Potentials
Journal article, 2023

The atomic scale dynamics of halide perovskites havea direct impactnot only on their thermal stability but also on their optoelectronicproperties. Progress in machine-learned potentials has only recentlyenabled modeling the finite temperature behavior of these materialsusing fully atomistic methods with near first-principles accuracy.Here, we systematically analyze the impact of heating and coolingrate, simulation size, model uncertainty, and the role of the underlyingexchange-correlation functional on the phase behavior of CsPbX3 with X = Cl, Br, and I, including both the perovskite andthe & delta;-phases. We show that rates below approximately 60 K/nsand system sizes of at least a few tens of thousands of atoms shouldbe used to achieve convergence with regard to these parameters. Bycontrolling these factors and constructing models that are specificfor different exchange-correlation functionals, we then assess thebehavior of seven widely used semilocal functionals (LDA, vdW-DF-cx,SCAN, SCAN+rVV10, PBEsol, PBE, and PBE+D3). The models based on LDA,vdW-DF-cx, and SCAN+rVV10 agree well with experimental data for thetetragonal-to-cubic-perovskite transition temperature in CsPbI3 and also achieve reasonable agreement for the perovskite-to-deltaphase transition temperature. They systematically underestimate, however,the orthorhombic-to-tetragonal transition temperature. All other models,including those for CsPbBr3 and CsPbCl3, predicttransition temperatures below the experimentally observed values forall transitions considered here. Among the considered functionals,vdW-DF-cx and SCAN+rVV10 yield the closest agreement with experiment,followed by LDA, SCAN, PBEsol, PBE, and PBE+D3. Our work providesguidelines for the systematic analysis of dynamics and phase transitionsin inorganic halide perovskites and similar systems. It also servesas a benchmark for the further development of machine-learned potentialsas well as exchange-correlation functionals.

Author

Erik Fransson

Chalmers, Physics, Condensed Matter and Materials Theory

Julia Wiktor

Chalmers, Physics, Condensed Matter and Materials Theory

Paul Erhart

Chalmers, Physics, Condensed Matter and Materials Theory

Journal of Physical Chemistry C

1932-7447 (ISSN) 1932-7455 (eISSN)

Vol. 127 28 13773-13781

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Subject Categories

Inorganic Chemistry

Condensed Matter Physics

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Materials Science

DOI

10.1021/acs.jpcc.3c01542

Related datasets

Data and code for "Phase transitions in inorganic halide perovskites from machine learning potentials: The impact of size, rate, and the underlying exchange-correlation functional" [dataset]

DOI: 10.5281/zenodo.7454223

More information

Latest update

12/12/2023