Molecular simulations of Perovskites CsXI3 (X = Pb,Sn) Using Machine-Learning Interatomic Potentials
Atefe Ebrahimi, Franco Pellegrini, Stefano De Gironcoli

TL;DR
This study develops machine learning interatomic potentials to accurately simulate phase transitions and structural properties of CsPbI3 and CsSnI3 perovskites, enabling large-scale, high-resolution molecular dynamics simulations.
Contribution
The paper introduces a novel MLIP framework within LATTE for simulating halide perovskites with near-experimental accuracy at reduced computational cost.
Findings
Pb perovskites have larger octahedral tilts and higher phase transition temperatures.
Sn perovskites show reduced tilts and lower energy barriers, indicating tunability.
MLIPs effectively bridge first principles accuracy with efficient large-scale simulations.
Abstract
Cesium based halide perovskites, such as CsPbI3 and CsSnI3, have emerged as exceptional candidates for next generation photovoltaic and optoelectronic technologies, but their practical application is limited by temperature dependent phase transitions and structural instabilities. Here, we develop machine learning interatomic potentials within the LATTE framework to simulate these materials with near experimental accuracy at a fraction of the computational cost compared to previous computational studies. Our molecular dynamics simulations based on the trained MLIPs reproduce energies and forces across multiple phases, enabling large scale simulations that capture cubic tetragonal orthorhombic transitions, lattice parameters, and octahedral tilting with unprecedented resolution. We find that Pb based perovskites exhibit larger octahedral tilts and higher phase transition temperatures than…
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