Work

Can simple exchange heuristics guide us in predicting magnetic properties of solids?

Year: 2025

Type: preprint

Abstract: 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.

Source: ChemRxiv

DOI: 10.26434/chemrxiv-2025-xj84d

Authors: Katharina Ueltzen, Aakash A. Naik, Christina Ertural, Philipp Benner, Janine George

Institutions: Federal Institute For Materials Research and Testing, Friedrich Schiller University Jena

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, European Research Council

Awards: G2777236169, G4231608669