Machine-learning-driven modelling of amorphous and polycrystalline BaZrS 3

Journal article

The chalcogenide perovskite material BaZrS3 is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's amorphous precursor as well as polycrystalline structures with complex grain boundaries. Using a bespoke machine-learned interatomic potential (MLIP) model for BaZrS3, we study the atomic-scale structure of the amorphous phase, quantify grain-boundary formation energies, and create realistic-scale polycrystalline structural models which can be compared to experimental data. Beyond BaZrS3, our work exemplifies the increasingly central role of MLIPs in materials chemistry and marks a step towards realistic device-scale simulations of materials that are gaining momentum in the fields of photovoltaics and photocatalysis.

Authors: Paşca, L; Liu, Y; Anker, AS; Steier, L; Deringer, VL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society of Chemistry
• Journal: Journal of Materials Chemistry A: materials for energy and sustainability
• Volume: 13
• Issue: 41
• Pages: 35447-35454
• Publication date: 2025-09-02
• Acceptance date: 2025-09-01
• DOI: 10.1039/d5ta04536c
• EISSN: 2050-7496
• ISSN: 2050-7488
• Language: English
• Keywords: Crystallite; Amorphous solid; Materials science; Crystallography; Metallurgy; Chemistry