Dresden 2026 – wissenschaftliches Programm

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MA: Fachverband Magnetismus

MA 58: Computational Magnetism II

MA 58.12: Vortrag

Freitag, 13. März 2026, 12:30–12:45, POT/0151

machine learning prediction of magnetic ordering in chiral induced multiferroic hybrid MOFs — •Gayathri Palanichamy, Biplab Sanyal, and Jonas Fransson — Materials Theory Division, Angstrom Laborartory, Uppsala Univesity, Sweden

Chiral hybrid metal organic frameworks with Ruddlesden Popper type halide layers provide a versatile platform for engineering multiferroicity by combining molecular chirality with transition metal magnetism. In this work, we develop a machine learning framework to identify multiferroic candidates in systems where chiral A-site organic molecules break inversion symmetry to induce ferroelectricity, while B-site transition metal centers govern magnetic ordering. First principles calculations confirm that the chiral distortion stabilizes a spontaneous polarization, and selected compositions exhibit coexisting ferroelectricity and ferromagnetism. To efficiently explore this large chemical and structural space, we construct feature descriptors capturing molecular chirality, distortion modes, metal ligand coordination metrics, and octahedral connectivity. Random Forest and Deep Neural Network models are trained to classify magnetic ground states (FM, AFM A, AFM C, AFM G) and achieve an R* score of 0.94, enabling reliable prediction of magnetic ordering across diverse chiral MOFs. Our approach also identifies previously unexplored chiral Ruddlesden Popper candidates with potential multiferroic behavior. This work establishes a data driven strategy toward building a chiral magnetic MOF repository, providing design principles for future spintronic and multifunctional materials.

Keywords: chiral metal organic frameworks; multiferroicity; machine learning; chiral induced ferroelectricity; magnetic ordering