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       Dr. Vijay Kumar Foundation
Computational Nanoscience
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Research on nanomaterials has gained importance in recent years because of the importance of these materials in daily life and for the development of future energy-efficient and environment-friendly designer materials. Nanomaterial properties are often vastly different from bulk properties and therefore there are unprecedented opportunities to explore materials behaviour at the nanoscale. While historically materials development has been primarily from experiments, computational materials science has now emerged as the third branch of materials research and development program, besides theory and experiment. Thanks to rapid advances in density functional theory and the exponential growth in available computing power, it is now possible to design materials at atomistic level with predictive capability and to fine tune properties relevant for specific applications. Optimization is the key for future development of an energy and materials efficient society and computer simulations are a very important tool for accelerated discovery and innovation of optimal designer materials.

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Discerning and exploiting patterns in chemical data lies at the heart of any systematic program for materials design. Mining the growing mass of experimental data on nanomaterials using the tools of atomistic modeling, statistical learning, and pattern recognition is necessary to discover complex quantitative relationships between chemical structure and the properties of materials. Such statistical methods use an array of computed structural descriptors and/or process parameters to predict the value of an experimental quantity, or to complement and leverage from first-principles computations (such as those using ab initio quantum chemistry and density functional theory), enabling quantitative predictions on many more systems than would be practicable with first-principles computations alone. This vision for materials informatics is shared by the US Materials Genome Initiative, recently launched by the US Office of Science and Technology Policy, to stimulate the development of models, simulation tools, and databases for predicting the properties and specific characteristics of nanoscale materials.

Our primary focus is in the following three major directions:
  • Inorganic nanomaterials - clusters, nanoparticles, nanowires, layered materials, & nanotubes (applications - devices, catalysis, environment, optical and magnetic)
  • Materials for energy applications - solar cell materials, Li ion battery materials, solid state lighting materials, fuel cells, H2 storage, CO2 capture, thermoelectric materials
  • Materials for biological applications - drug delivery, sensors, imaging, drug design, nanomaterials in therapy such as using gold nanoparticles to cure cancer, multifunctional materials. This will involve research on a combination of inorganic -organic materials.

Pt Clusters                 
Au nanoparticles in bioapplications
Li battery materials            

Bioinformatics and Cheminformatics

Design of Organic Dyes and Organic/Polymer Semiconductors

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