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MM: Fachverband Metall- und Materialphysik

MM 27: Data Driven Material Science: Big Data and Workflows IV

MM 27.2: Vortrag

Dienstag, 19. März 2024, 13:45–14:00, C 243

Machine learning modelling of local magnetic moments in Fe-based intermetallic compounds — •Bo Zhao¹, Kun Hu², and Hongbin Zhang¹ — ¹Institute of Materials Science, Technische Universität Darmstadt, Peter-Grünberg-Str. 2, 64287 Darmstadt, Germany — ²School of Materials Science and Engineering, Central South University, Changsha, 410083, Hunan, PR China

It is well known that the magnitude of magnetic moments is determined by the local crystalline environments as verified by studying the dimensional crossover behavior of a few representative systems such as Fe. To further generalise and quantify the mapping between the crystal environments and magnetic moments, we carried out machine learning modelling on Fe-based intermetallic compounds, with the corresponding magnitude of magnetic moments for Fe atoms varying between zero and four Bohr magneton (the atomic limit). Using the symmetry-adapted smooth overlap of atomic positions (SOAPs) as descriptors, it is observed that 2374 data for binary and ternary compounds collected from Materials Project are not sufficient for a reliable modelling. We further enriched the dataset by performing high-throughput calculations on Fe-(B, Co, Ti, Rh) binary intermetallic compounds, and the corresponding accuracy of the machine learning modelling is over 90% across all the systems. The results are further understood based on the Stoner model. Our work establishes a valid approach to model the physical properties dependent on the local crystalline environments.