Presentation Information
[2F06]Interface Tuning and Chemical Classification of Ultrathin MnTe Films Grown on Fe(001) Using STM/STS with Machine Learning for Epitaxial Growth of 2D van der Waals Magnets
*Haruto Seki1, Kenji Nawa2,3,4, Chiharu Mitsumata5,6, Toyo Kazu Yamada1,7 (1. Graduate School of Science and Engineering, Chiba University, 2. Graduate School of Engineering, Mie University, 3. Research Center for Magnetic and Spintronic Materials, National Institute for Materials Science, 4. National Institute of Advanced Industrial Science and Technology, Research Institute for Hybrid Functional Integration, 5. Graduate School of Pure and Applied Science, University of Tsukuba, 6. TREMS, University of Tsukuba, 7. Molecular Chirality Research Centre, Chiba University)
We studied the epitaxial growth of sub-monolayer Mn films on a Te-covered Fe(001) surface at 300 K, followed by annealing up to 653 K. Using STM/STS, machine learning, and DFT, we identified six distinct surface regions: bilayer Te (~15%), bilayer MnTe alloy (~15%), Te-on-Mn (~2%), Mn-on-Te (~3%), and MnTe alloys with Mn-rich (~33%) and Te-rich (~13%) areas. Annealing at ~650 K promotes the formation of a bilayer MnTe alloy with an atomically flat interface. This work shows that machine learning applied to STS enables fast, accurate identification of complex chemical and electronic structures, surpassing traditional analysis methods and accelerating the development of 2D magnetic materials [Seki, et al., ACS Applied Nano Materials (2025)].