Diverse polymorphism in Ruddlesden-Popper chalcogenides

TL;DR

This study uses machine learning and molecular dynamics to explore structural diversity and phase transitions in Ruddlesden-Popper chalcogenides, revealing new polymorphs and complex behaviors relevant for functional applications.

Contribution

Developed a high-accuracy machine-learned interatomic potential to predict new polymorphs and phase behaviors in RP chalcogenides, expanding understanding of their structural evolution.

Findings

Predicted new polymorphs for each n-value in Ba-based RP chalcogenides.

Identified negative thermal expansion in n=1 phase.

Discovered layer-dependent tilt patterns in phases with n≥4.

Abstract

Ruddlesden-Popper (RP) chalcogenides are stable, non-toxic candidates for optoelectronic or thermoelectric applications. The structural diversity of RP oxides is already exploited to tune properties or achieve more advanced functionalities like multiferroicity, however, little is known about the structural evolution of RP chalcogenides. In this work, we develop a high-accuracy machine-learned interatomic potential to run large-scale molecular dynamics simulations on $Ba_{n+1}Zr_nS_{3n+1}$ for $n=1$ to $n=6$. We predict new polymorphs for each $n$-value, calculate their corresponding phase transition temperatures, and validate our approach through comparison to published experimental results. We find that the $n=1$ phase exhibits negative thermal expansion, that $n=1$ and $n=3$ undergo unusual ascending symmetry breaking, and that phases with $n\geq4$ form layer-dependent tilt patterns previously unreported for inorganic RP materials. This unique behaviour results from competition between octahedral rotations and rumpling at the rocksalt interface, and suggests new strategies for accessing advanced functionalities.