PROJECT NAME
METAL-ORGANIC FRAMEWORKS AND MIXED-MATRIX POLYMER MEMBRANES: AI-ASSISTED DESIGN AND STRUCTURAL ENGINEERING USING SYNCHROTRON RADIATION
About project
Principal Researcher
Postdoc
The goal
The goal of the research project is to develop a comprehensive non-destructive in-situ and operando nanometrological X-ray spectral characterization of the parameters of the local atomic and electronic structures of materials using modern instrumental methods, including the latest generation of synchrotron radiation sources
Field of sciences
Chemistry and Materials Sciences
Topics of Ongoing Projects at The Smart Materials Research Institute
  • Theoretical and experimental study of the photoswitching dynamics of molecular magnets: preparation for femtosecond X-ray experiments at XFEL;
  • Study of materials for energy conversion using synchrotron radiation and supercomputer modeling;
  • Development of methods for constructing hybrid materials based on three-dimensional metal-organic coordination polymers and photochromic compounds of various structures;
  • Picometer-scale diagnostics of 3D local atomic structure parameters of nanomaterials based on XANES spectroscopy;
  • Palladium nanocatalysts in key oxidation reactions: studies using in situ, operando, and modulated-excitation methods with synchrotron radiation;
  • Rational design of palladium-based catalysts for C–H bond activation and ruthenium-based catalysts for C–O bond hydrogenation: from operando X-ray absorption spectroscopy to multispectral diagnostics using machine learning;
  • New efficient nanocatalysts for photostimulated "green" hydrogen production reactions: computational design, laboratory and synchrotron studies using machine learning technologies;
  • Nanocomposites for X-ray photodynamic therapy of deep-seated tumors in oncology;
  • AI-controlled robotic station at a synchrotron radiation source for accelerated development of new advanced materials and their real-time diagnostics;
  • Full-cycle technologies for the rapid development of functional materials for the low-carbon economy under AI control;
  • New functional nanomaterials for applications in catalytic processes and in energy storage and conversion technologies;
  • Theoretical and experimental study of the photoswitching dynamics of molecular magnets: preparation for femtosecond X-ray experiments at XFEL;
  • Understanding the kinetics of photoelectrocatalytic reactions based on in situ diagnostics using synchrotron radiation and artificial intelligence;
  • Frontier Laboratory of X-ray Spectral Nanometrology.
Research Areas
  • Nanomaterials and nanotechnology
  • metal-organic frameworks
  • membrane technologies
  • computer science, artificial intelligence
  • synchrotron-neutron research.
Equipment
  • Microfluidic synthesis setups;
  • Laboratory and synchrotron X-ray sources (XAFS/XANES/EXAFS beamlines);
  • A complex of analytical equipment for polymer and composite materials (GPC/SEC, HPLC, TGA/DSC, FTIR, SEM);
  • A supercomputer cluster, including specialized nodes for AI-based computations;
  • Synchrotron radiation centers;
  • The only laboratory R-XAS spectrometer (Rigaku) in Europe.
Principal Researcher
  • Soldatov Alexander Vladimirovich
    Soldatov Alexander Vladimirovich, Doctor of Physical and Mathematical Sciences, Professor, Scientific Director of the "Materials Science and Synchrotron-Neutron Research" direction at SFedU, Acting Director of the International Research Institute of Intelligent Materials of SFedU, Head of the scientific laboratory "Frontier Laboratory of X-ray Spectral Nanometrology."

    The field of science and scientific directions:
    Nanotechnology, Materials, Physics

Soldatov A.V. was repeatedly a member and chairman of the program committees of international scientific conferences.
Soldatov A.V. was repeatedly a member and chairman of the program committees of international scientific conferences.
The results of his research have received international recognition, and he has repeatedly worked as a visiting professor at leading foreign educational centers (University of Rome (Italy), University of Western Ontario (Canada), University of Antwerp (Belgium), University of Nijmegen (Netherlands), at the Free University of Berlin (Germany). Member of the editorial boards of a number of scientific journals, including the Journal of Chemical Physics of the Russian Academy of Sciences, Surface: Synchrotron and Neutron Research of the Russian Academy of Sciences, and Russian Nanotechnology.
He has successfully supervised the implementation of over 80 research grants and projects, including over 30 international projects.

Soldatov A.V. is one of the highly cited scientists in Russia ("Active Russian Scientist -2023". He has over 1,000 scientific publications (including over 450 articles in highly rated international peer-reviewed scientific journals).
He has trained 29 candidates and 5 Doctors of Sciences. Currently, he directs the training of 6 graduate students and 3 doctoral students.
Actively conducts pedagogical work. He has prepared more than 60 teaching aids and three electronic multimedia textbooks.
He developed and implemented the training courses "Solid State Physics", "X-ray spectroscopy", "Nanoscale structure of matter", "Electronic structure of matter", "Physics of nanoclusters", "Multiscale computer modeling"
Postdocs
  • Role of the Postdoctoral Fellow
    The postdoc will be involved in cooperation with a team of specialists in the field of chemistry, physics and computer modeling. The postdoc must form a team around him/herself that will be able to solve diagnostic problems in the process of synthesizing new materials using machine learning methods.
  • Candidate Requirements
    • age up to 39;
    • PhD degree (or equivalent) in materials science, applied mathematics, chemistry, physics;
    • fluent English (at least upper intermediate level - B2);
    • at least 3 publications (Scopus and Web of Science) on the topic of the project;
    • experience in data analysis and machine learning, with a focus on applications in spectroscopy, cheminformatics and materials science;
    • general programming skills related to machine learning (preferably Python with the ability to use tools: Pandas, numpy, sklearn, scipy, statmodels, pytorch\jax\tensorflow);
    • experience in writing scientific articles and literature reviews.
  • Terms and Conditions
    Salary – 120,000 rubles.
    Additional support –support in obtaining a visa, official housing, assistance in employment and education of family members, internships, trips, visits to medical institutions, Russian language courses, etc.
The estimated duration of work is 2-3 years
Working languages
Russian
English