Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916772706254848 |
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| author | Paşca, Laura-Bianca Liu, Yuanbin Anker, Andy S. Steier, Ludmilla Deringer, Volker L. |
| author_facet | Paşca, Laura-Bianca Liu, Yuanbin Anker, Andy S. Steier, Ludmilla Deringer, Volker L. |
| contents | The chalcogenide perovskite material BaZrS$_{3}$ 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 BaZrS$_{3}$, 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 BaZrS$_{3}$, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01517 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$ Paşca, Laura-Bianca Liu, Yuanbin Anker, Andy S. Steier, Ludmilla Deringer, Volker L. Materials Science The chalcogenide perovskite material BaZrS$_{3}$ 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 BaZrS$_{3}$, 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 BaZrS$_{3}$, 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. |
| title | Machine-learning-driven modelling of amorphous and polycrystalline BaZrS$_{3}$ |
| topic | Materials Science |
| url | https://arxiv.org/abs/2506.01517 |