Presently we put our efforts into the development of new-generation semiempirical methods based on successful approach embedded into the OMx methods.
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.
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.
We use our own (semiempirical UNO–CI) and DFT methods to explain experimentally observed photophysical phenomena and predict photophysical properties of unknown compounds.
We apply quantum various mechanical methods to elucidate reaction mechanisms and calculate reaction energetics. Our effort is focused on alkane activation and fullerene functionalization.