Exploring noncollinear magnetic energy landscapes with Bayesian optimization
Using Bayesian optimization, we efficiently map large magnetic configuration spaces to identify ground states and model complex magnetic energy landscapes.
Authors
- Lorenzo Celiberti (ORCID: https://orcid.org/0000-0001-8362-6428)
- Patrick Rinke (ORCID: https://orcid.org/0000-0003-1898-723X)
- Cesare Franchini (ORCID: https://orcid.org/0000-0002-7990-2984)
- Milica Todorović (ORCID: https://orcid.org/0000-0003-0028-0105)
- Jakob Baumsteiger (ORCID: https://orcid.org/0009-0003-2694-0842)
Institutions
- University of Vienna (AT)
- University of Turku (FI)
- Munich Center for Machine Learning
- Technical University of Munich (DE)
- University of Bologna (IT)
- Aalto University (FI)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4dd00402g
- Citations
- 1
- Primary Topic
- Theoretical and Computational Physics
- Type
- article
- Field-Weighted Citation Impact
- 1.26