Work

High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics

Year: 2025

Type: article

Abstract: Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and Mössbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to such properties. It is observed that magnetic moments and Mössbauer parameters provide complementary insights into the local crystalline environment, where the statistical features cannot be captured using phenomenological models. Furthermore, we find that the scarcity of the existing data in the Materials Project (MP) poses a significant challenge in developing predictive machine learning models, whereas smooth overlap of atomic positions (SOAP)-based descriptors can be applied for reliable modeling of the enriched datasets with extra structural prototypes. This work advances the mapping of local crystalline structures to magnetic and spectroscopic properties, bridging the gap between empirical observations and theoretical models.

Source: Physical review. B./Physical review. B

DOI: 10.1103/brpz-w2tk

Authors: Bo Zhao, Hongbin Zhang

Institution: Technische Universität Darmstadt

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung

Award: SFB 1487: Eisen, neu gedacht!