Machine-learning-driven modelling of amorphous and polycrystalline BaZrS<sub>3</sub>
We present a bespoke machine-learned interatomic potential (MLIP) model for the emerging solar-cell material BaZrS 3 . This potential can be used to simulate both the amorphous precursor and the polycrystalline material with complex grain boundaries.
Authors
- Andy S. Anker (ORCID: https://orcid.org/0000-0002-7403-6642)
- Yuanbin Liu (ORCID: https://orcid.org/0000-0002-5948-7031)
- Volker L. Deringer (ORCID: https://orcid.org/0000-0001-6873-0278)
- Laura-Bianca Paşca
- Ludmilla Steier
Institutions
- University of Oxford (GB)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta04536c
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- UK Research and Innovation
- Novo Nordisk
- Novo Nordisk Fonden
- Engineering and Physical Sciences Research Council