Krishna Rajan

Learn More
The intracellular bacterium Francisella tularensis survives in mammals, arthropods, and freshwater amoeba. It was previously established that the conventional media used for in vitro propagation of this microbe do not yield bacteria that mimic those harvested from infected mammals; whether these in vitro-cultivated bacteria resemble arthropod- or(More)
Field applications of existing sensing solutions to structural health monitoring (SHM) of civil structures are limited. This is due to economical and/or technical challenges in deploying existing sensing solutions to monitor geometrically large systems. To realize the full potential of SHM solutions, it is imperative to develop scalable cost-effective(More)
This paper develops a statistical learning approach to identify potentially new high-temperature ferroelectric piezoelectric perovskite compounds. Unlike most computational studies on crystal chemistry, where the starting point is some form of electronic structure calculation, we use a data-driven approach to initiate our search. This is accomplished by(More)
In engineering design, we are constantly faced with the need to describe the behavior of complex engineered systems for which there is no closed-form solution. There is rarely a single multiscale theory or experiment that can meaningfully and accurately capture such information primarily due to the inherently multivariate nature of the variables influencing(More)
The advent of Local Electrode Atom Probe (LEAP) tomography is revolutionizing materials science by enabling near atomic scale imaging of materials. Analysis of three-dimensional atom probe tomography (APT) data holds the promise of relating combinatorial arrangement of atoms to material properties and enable better design and synthesis of complex materials.(More)
Rational materials design based on prior knowledge is attractive because it promises to avoid time-consuming synthesis and testing of numerous materials candidates. However with the increase of complexity of materials, the scientific ability for the rational materials design becomes progressively limited. As a result of this complexity, combinatorial and(More)
This paper predicts the bandgaps of over 200 new chalcopyrite compounds for previously untested chemistries. An ensemble data mining approach involving Ordinary Least Squares (OLS), Sparse Partial Least Squares (SPLS) and Elastic Net/Least Absolute Shrinkage and Selection Operator (Lasso) regression methods coupled to Rough Set (RS) and Principal Component(More)
In this work, it is shown that for the first time that, using information-entropy-based methods, one can quantitatively explore the relative impact of a wide multidimensional array of electronic and chemical bonding parameters on the structural stability of intermetallic compounds. Using an inorganic AB2 compound database as a template data platform, the(More)
A data driven discovery strategy based on statistical learning principles is used to discover new correlations between electronic structure and catalytic activity of metal surfaces. From the quantitative formulations derived from this informatics based model, a high throughput computational framework for predicting binding energy as a function of surface(More)