Can simple exchange heuristics guide us in predicting magnetic properties of solids?
A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this heuristic on databases of magnetic structures. The heuristic is partly satisfied in magnetic structures from experiment, and exceptions can be rationalized. We further show that incorporating this heuristic into machine learning models for magnetism improves the prediction.
Authors
- Aakash Ashok Naik (ORCID: https://orcid.org/0000-0002-6071-6786)
- Philipp Benner (ORCID: https://orcid.org/0000-0002-0912-8137)
- Christina Ertural (ORCID: https://orcid.org/0000-0002-7696-5824)
- Katharina Ueltzen (ORCID: https://orcid.org/0009-0003-2967-1182)
- Janine George (ORCID: https://orcid.org/0000-0001-8907-0336)
Institutions
- Federal Institute For Materials Research and Testing (DE)
- Friedrich Schiller University Jena (DE)
Publication Details
- Journal
- ChemRxiv
- Published
- 2025-08-14
- DOI
- https://doi.org/10.26434/chemrxiv-2025-xj84d
- Citations
- 1
- Primary Topic
- Magnetic Properties and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.43
Funders
- European Commission
- European Research Council