Phase Transitions in Inorganic Halide Perovskites from Machine-Learned Potentials
Artikel i vetenskaplig tidskrift, 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.


Erik Fransson

Chalmers, Fysik, Kondenserad materie- och materialteori

Julia Wiktor

Chalmers, Fysik, Kondenserad materie- och materialteori

Paul Erhart

Chalmers, Fysik, Kondenserad materie- och materialteori

Journal of Physical Chemistry C

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

Vol. 127 28 13773-13781

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Relaterade dataset

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

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