Maxim A Ziatdinov

Maxim A Ziatdinov

Post-Doctoral Research Associate


Maxim Ziatdinov is a postdoctoral research associate at the Center for Nanophase Materials Sciences and Institute for Functional Imaging of Materials at Oak Ridge National Laboratory. He received his PhD degree in materials science and engineering from the Tokyo Institute of Technology in 2014. His current research focuses on scanning probe microscopy of strongly correlated materials, including high-temperature superconductors and Kitaev materials, as well as on application of machine learning and pattern recognition tools to extracting new knowledge from experimental datasets.


  1. Extracting new knowledge from scanning probe and electron microscopy experiments within physics-constrained frameworks of machine learning and multivariate analysis. Nanoscale local inhomogeneity associated with intertwined structural and electronic orders in many quantum materials with strong electron correlation may have a profound and non-random effect on their (technologically relevant) macroscopic properties. We are developing new approaches for analysing local probe microscopic and spectroscopic data from functional materials with unconventional electronic and magnetic properties within physics-robust frameworks of machine learning and multivariate statistics. Our methods allow extracting and accurate mapping of statistically significant structure-property relationships on a local scale in an automated fashion of a complete information extraction.
  2. Thin films oxides. Electronic, magnetic and structural properties of quasi-2D thin films may be different from those in the bulk, and can also be heavily influenced (and, ideally, controlled) by a substrate. I am interested in in-situ growth of oxide thin films by means of pulsed laser deposition and their subsequent characterization using a combination of various scanning probe and spectroscopic techniques.
  3. Materials informatics. I am a part of a collaboration that applies "Big Data" style analysis to existing scientific literature for accelerating design of novel materials with specific functionalities.


  1. M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. K. Vasudevan, S. Jesse, and S. V. Kalinin. Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations. ACS Nano 2017, 11, 12742.
  2. M. Ziatdinov, A. Maksov, S. V. Kalinin. Learning surface molecular structures via machine vision. NPJ Computational Materials 2017, 3, 1.
  3. M. Ziatdinov, H. Lim, S. Fujii, K. Kusakabe, M. Kiguchi, T. Enoki, and Y. Kim. Chemically induced topological zero mode at graphenic armchair edge. Phys. Chem. Chem. Phys. 2017, 19, 5145.
  4. M. Ziatdinov, A. Banerjee, A. Maksov, T. Berlijn, W. Zhou, H. B. Cao, J. Q. Yan, C. A. Bridges, D. G. Mandrus, S. E. Nagler, A. P. Baddorf and S. V. Kalinin. Atomic-scale observation of structural and electronic orders in the layered compound a -RuCl3. Nature Communications 2016, 7, 13774.
  5. M. Ziatdinov, A. Maksov, L. Li, A. S. Sefat, P. Maksymovych and S. V. Kalinin. Deep data mining in a real space: separation of intertwined electronic responses in a lightly doped BaFe2As2. Nanotechnology 2016, 27, 475706.
  6. R. K. Vasudevan, M. Ziatdinov, S. Jesse, S. V. Kalinin. Phases and Interfaces from Real Space Atomically Resolved Data: Physics-Based Deep Data Image Analysis. Nano Letters 2016, 16, 5574.
  7. M. Ziatdinov, S. Fujii, M. Kiguchi, T. Enoki, S. Jesse and S. V. Kalinin. Data mining graphene: correlative analysis of structure and electronic degrees of freedom in graphenic monolayers with defects. Nanotechnology 2016, 27, 495703.
  8. N. Laanait, M. Ziatdinov, Q. He and A. Borisevich. Identifying local structural states in atomic imaging by computer vision. Advanced Structural and Chemical Imaging 2016, 2, 14.
  9. S. Fujii, M. Ziatdinov, S. Higashibayashi, H. Sakurai, M. Kiguchi. Bowl Inversion and Electronic Switching of Buckybowls on Gold. Journal of the American Chemical Society 2016, 138, 12142.
  10. R. K. Vasudevan, M. Ziatdinov, C. Chen and S. V. Kalinin. Analysis of citation networks as a new tool for scientific research. MRS Bulletin 2016, 41, 1009 (invited).
  11. M. Ziatdinov, S. Fujii, K. Kusakabe, M. Kiguchi, T. Mori, T. Enoki. Direct Imaging of Monovacancy-Hydrogen Complexes in a Single Graphitic Layer. Physical Review B 2014, 89, 155405.
  12. S. Fujii, M. Ziatdinov, M. Ohtsuka, K. Kusakabe, M. Kiguchi, T. Enoki. Role of Edge Geometry and Chemistry in the Electronic Properties of Graphene Nanostructures. Faraday Discussions 2014, 173, 173.
  13. M. Ziatdinov, S. Fujii, K. Kusakabe, M. Kiguchi, T. Mori, T. Enoki. Visualization of Electronic States on Atomically Smooth Graphitic Edges with Different Types of Hydrogen Termination. Physical Review B 2013, 87, 115427.


User Facility

Contact Information