Grants

Driving by artificial intelligence automated laboratory at synchrotron radiation source for accelerated development of novel advanced materials and their on-the-fly characterization
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Nanocatalysts for photostimulated green hydrogen production: molecular design and advanced characterization assisted by machine learning methods
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Establishing the relationships between the vibrational spectra of zeolites by quantum-chemical modelling and machine learning methods
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Investigation of direct methane-methanol conversion mechanism over the Cu sites in Cu-exchanged zeolites
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Digital model of the experimental physical environment for diagnosing nanomaterials based on synchtron radiation for training deep reinforcement learning agents
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Metal-organic frameworks (MOFs) represent a new class of crystalline materials with extremely high pore volume and specific surface area. MOFs have a great variety of potential applications.
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Palladium-based nanomaterials for catalytic applications. In situ, operando and time-resolved studies using synchotron-based techniques.
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FitIt is a new approach to fit spectra using multidimensional interpolation approximation. FitIt allows obtaining information about the local atomic structure from XANES spectra.
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Three-dimensionally confined semiconductor quantum dots with unique physical and chemical properties for application in physics, chemistry, biology and medicine
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X-ray absorption spectroscopy for 3D atomic and electronic structures of materials with and without long-range order: nanoparticles, molecules, biological systems, etc.
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Multiscale computer modeling of atomic structure and electronic properties of nanomaterials
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