Ruddlesden-Popper (RP) chalcogenides are an emerging class of layered semiconductors with tunable properties and chemical stability, making them promising candidates for a wide range of functional applications. Over the past four decades, the structural diversity of RP oxides has been exploited to realize advanced functionalities; however, similar strategies have not yet been applied to RP chalcogenides, whose structural behavior remains poorly understood. In this study, we develop a high-accuracy machine-learned interatomic potential to perform large-scale molecular dynamics simulations of the homologous RP series ${\mathrm{Ba}}_{n+1}{\mathrm{Zr}}_n{\mathrm{S}}_{3n+1}$. We identify new polymorphs for each $n$ value, predict the corresponding phase transition temperatures, and validate our approach through comparison with existing experimental data. We find that the $n=1$ phase exhibits in-plane negative thermal expansion, that the $n=1$ and $n=3$ phases undergo unusual ascending symmetry breaking, and that phases with $n\ge 3$ develop layer-dependent tilt patterns not previously observed in inorganic RP compounds. This distinctive behavior arises from the interplay between ${\mathrm{ZrS}}_6$ octahedral rotations and BaS rumpling at the perovskite-rocksalt interface, suggesting new strategies for realizing advanced functionalities and tuning properties in RP chalcogenides.

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• Crystal structure
• First-principles calculations
• Phase transitions
• Energy materials
• Layered crystals
• Layered semiconductors
• Perovskites
• Density functional theory
• Machine learning
• Molecular dynamics