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Research Interests


Development of the Effective Fragment Potential (EFP) based QM/MM Methods

Theoretical modeling of a chemical process in solution or on a surface is challenging due to the dramatic increase in the number of degrees of freedom of the combined system, exacerbated by the complexity and diversity of the underlying mechanisms and pathways. This makes computer simulations very demanding. In our group, we develop robust computational tools that will facilitate accurate and revealing investigations of chemical and biological processes in an environment. In particular, we combine state-of-the-art ab initio excited state methods in the equation-of-motion coupled-cluster (EOM-CC) family and the sophisticated model potential called the effective fragment potential (EFP) method in a novel and efficient hybrid QM/MM (quantum mechanics/molecular mechanics) method. Top

Mechanical Properties of Grafted Carbon Nanotubes

Exceptional strength and stiffness of chemically functionalized, doped and grafted carbon nanotubes (CNT) draw considerable attention last years. In particular, mechanical properties of CNT have been investigated using various ab initio and continuum mechanics approaches. However, due to extremely large size of the system the nanotube composites require enormous computational resources to be investigated by full-electron ab initio methods. We developed computationally effective combined ab initio/continuum mechanics approach to estimate mechanical properties of CNT composites. The approach is based on controlled deformation of the CNT composite with full-electron ab initio evaluation of the electronic energy of the system. Top

Multiscale Simulation of Biological Neuronal Networks

Biological neuronal networks are useful model for studies in neuroscience. Recent results on remote control of neurons with help of photo activated labels (PAL) are promising. Application of PAL provides a new tools for neuroscience research. In order to simulate the experiments on the networks with PAL attached neurons we employ multiscale approach to simulate the neuronal network with PAL attached. Our model incorporates high level ab initio simulations on PAL molecule and use Hodgkin-Huxley type model of a neuron for evaluation of whole network. Top

Biologically Inspired Methods of Computations

Fast expansion of global computational networks, development of web 2.0 technologies, and growth of social networks leads to development of a new paradigm of scientific research. I am working on development of computational framework for multiscale simulations from single molecule to level of organism. At each level of modeling different types of variables can be used. The choice of the optimal variables comes from trade-off between accuracy of the model and computational effectiveness. The choice of particular variable set depends on spatial-temporal scale can be considered as process similar to biological evolution. Currently available global network technologies can help to reveal this evolution and leads to development of new multiscale models in a wide range of disciplines. Top
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