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
