Stefano De Gironcoli | Searcharxiv

arXiv2025

Molecular simulations of Perovskites CsXI3 (X = Pb,Sn) Using Machine-Learning Interatomic Potentials

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 Sn based analogues, reflecting stronger bonding and enhanced structural stability, whereas Sn based perovskites display reduced tilts and lower barriers, suggesting tunability through compositional or interface engineering. Beyond these systems, our work demonstrates that MLIPs can bridge first principles accuracy with simulation efficiency, providing a robust framework for exploring phase stability, anharmonicity, and rational design in next generation halide perovskites.

cond-mat.mtrl-sci↗

arXiv2022

Compact atomic descriptors enable accurate predictions via linear models

We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecules and solids. We find that such a simple regression framework performs on par with state-of-the-art machine learning methods which are, in most cases, more complex and more computationally demanding. Subsequently, we look for ways to sparsify the descriptor and further improve the computational efficiency of the method. To this aim, we use both principal component analysis and least absolute shrinkage operator regression for energy fitting on six single-element datasets. Both methods highlight the possibility of constructing a descriptor that is four times smaller than the original with a similar or even improved accuracy. Furthermore, we find that the reduced descriptors share a sizable fraction of their features across the six independent datasets, hinting at the possibility of designing material-agnostic, optimally compressed, and accurate descriptors.

physics.comp-ph↗