Diverse Polymorphism in Ruddlesden-Popper Chalcogenides

Area of Science:

• Materials Science
• Solid State Chemistry
• Computational Materials Science

Background:

• Ruddlesden-Popper (RP) chalcogenides are layered semiconductors with tunable properties and stability.
• While RP oxides are well-studied, RP chalcogenides' structural behavior is poorly understood.
• Exploiting structural diversity in RP chalcogenides could lead to advanced functionalities.

Purpose of the Study:

• To investigate the structural behavior of the homologous RP series Ba_{n+1}Zr_{n}S_{3n+1}.
• To develop a predictive model for understanding phase transitions and structural properties.
• To identify new polymorphs and their characteristics in RP chalcogenides.

Main Methods:

• Development of a high-accuracy machine-learned interatomic potential.
• Large-scale molecular dynamics simulations of Ba_{n+1}Zr_{n}S_{3n+1} series.
• Validation of simulation results against experimental data.

Main Results:

• Identification of new polymorphs for various n values in the Ba_{n+1}Zr_{n}S_{3n+1} series.
• Prediction of phase transition temperatures for the identified polymorphs.
• Observation of in-plane negative thermal expansion in the n=1 phase.
• Discovery of unusual ascending symmetry breaking in n=1 and n=3 phases.
• Observation of novel layer-dependent tilt patterns in phases with n≥3.
• Correlation of observed behaviors with the interplay of ZrS_{6} octahedral rotations and BaS rumpling.

Conclusions:

• The study reveals unprecedented structural behaviors in RP chalcogenides.
• The findings suggest new avenues for tuning properties and realizing advanced functionalities.
• Machine learning potentials are effective for exploring complex materials systems like RP chalcogenides.