Development of Quantum Chemical Methods

We develop new-generation ODMx semiempirical methods.

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Machine Learning in Chemistry

Improvement of a semiempirical quantum chemical (SQC) method using Machine Learning (ML)

We apply machine learning (ML) methods to improve accuracy of less accurate quantum mechanical (QM) methods—DFT and especially semiempirical quantum chemical (SQC) methods—and use them for calculating various molecular properties with reasonable accuracy and low computational cost. We also use ML for representing very accurately potential energy surfaces and for nonadiabatic excited-state dynamics.

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Dependence of exciton binding energy, optical and transport band gap from dopant in PAH

We apply ab initio, DFT and semi-empirical methods to calculate different physicochemical and first of all electronic properties of compounds that are already used or are prospective materials for molecular (nano)electronics.

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Photophysics of Organic Molecules

ExcitonFormationWe use our own (semiempirical UNO–CI) and DFT methods to explain experimentally observed photophysical phenomena and predict photophysical properties of unknown compounds.

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Reaction Mechanisms and Energetics

We apply quantum various mechanical methods to elucidate reaction mechanisms and calculate reaction energetics. Our effort is focused on alkane activation and fullerene functionalization.

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