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

Bo Zhao and Hongbin Zhang*

  • *Contact author: hongbin.zhang@tu-darmstadt.de

Visualization-driven physics-informed modeling of energetics with demonstrated transfer between configurationally isomorphic substitutional alloys

Gholamhosain Haidari and Zahra Mosleh

Results in Physics 87 108722 (2026)

Machine Learning Predictions of Experimental Mössbauer Properties of Fe-Containing Molecular Complexes and Protein Models

Ioannis Skoulidas, Somayeh Tavasolikejani, Daisy Morgan, Jia-Min Chu, and Yong Zhang

Journal of Chemical Information and Modeling (2026)

Data-Driven Deciphering Structure–Mössbauer Spectroscopy Relationships in Iron-Based Compounds

Tao Yang, Jiaqing Zhang, Haotian Chen, Xiaoze Yuan, Yuwei Zhou, Wenping Guo, Xingchen Liu, Xiaotong Liu, and Jinjia Liu

J. Phys. Chem. Lett. 17 2111 (2026)

Equivariant machine learning of electric field gradients—Predicting the quadrupolar coupling constant in the MAPbI3 phase transition

Bernhard Schmiedmayer, Jop W. Wolffs, Gilles A. de Wijs, Arno P. M. Kentgens, Jonathan Lahnsteiner, and Georg Kresse

The Journal of Chemical Physics 163 214110 (2025)

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