First-principles based modeling of molecules and materials has made significant progress in the past four decades. In the early days, computations were primarily used as a tool to rationalize experimental results. However, the advent of supercomputers and development of fast and improved algorithms have now enabled researchers not only to gain key insights about materials but also to predict new materials with improved properties. Many of these predictions have been successfully verified in the subsequent laboratory experiments; thus paving the way for a new approach of materials discovery. In this presentation, we will talk about this combined approach of computational materials design, rationalization-prediction-verification. The concept of so called 'descriptors' is important in this context. These are key parameters used to screen for the materials with desired properties from a pool of hundreds of thousands of other materials. Two specific examples pertaining to our research in the area of energy conversion and storage will be discussed. The first example is related to the discovery of new heterogeneous catalysts for very selective conversion of alkanes to alkenes. The second example is related to battery research, in particular, new results obtained for lithium-oxygen and lithium-sulfur batteries will be discussed.

Replace burette and pipette with mouse and keyboard: First-principles based modeling of materials for energy storage and conversion

248th National Meeting (2014)

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