Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr$_3$ and MAPbI$_3$
Erik Fransson, J. Magnus Rahm, Julia Wiktor, and Paul Erhart

TL;DR
This study maps the free energy landscape of CsPbBr3 and MAPbI3 halide perovskites, revealing metastability, transition mechanisms, and phase diagrams crucial for improving their photovoltaic stability.
Contribution
It provides detailed atomic-scale insights into phase transitions and metastable states of CsPbBr3 and MAPbI3 using advanced simulations and machine learning potentials.
Findings
CsPbBr3 exhibits small free energy differences near transition temperatures.
MAPbI3 has complex transition behavior with large barriers and multiple phases.
First-order transition characteristics are confirmed through latent heat and structural changes.
Abstract
Halide perovskites have emerged as a promising class of materials for photovoltaic applications. A challenge in these applications is how to prevent the crystal structure from degradation to photovoltaically inactive phases, which requires an understanding of the free energy landscape of these materials. Here, we uncover the free energy landscape of two prototypical halide perovskites, CsPbBr and MAPbI via atomic scale simulations using umbrella sampling and machine-learned potentials. For CsPbBr we find very small free energy differences and barriers close to the transition temperatures for both the tetragonal-to-cubic and the orthorhombic-to-tetragonal transition. For MAPbI, however, the situation is more intricate. In particular the orthorhombic-to-tetragonal transition exhibits a large free energy barrier and there are several competing tetragonal phases. Using…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPerovskite Materials and Applications · Machine Learning in Materials Science · Solid-state spectroscopy and crystallography
