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Aaron Gilad Kusne
Research interests:
Data-mining for Rapid Analysis of Massive Materials Science Databases. Developing data-mining techniques to accelerate the discovery of advanced materials. These new data-mining techniques integrate solid state physics, lattice and symmetry analysis, and information theory. The data-mining techniques are run both offline and online during sample characterization to provide live guidance to the experimentalist and improve data collection. Techniques of interest include (but are not limited to) sparse kernel machines, latent variable analysis, and Bayesian analysis.
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Prior Work:
Theory of Field Induced Quantum Tunneling. Developed nist-equations for modeling field emission mechanisms of conductive ellipsoidal field emitters and established new metrics for comparing device performance. Performance metrics studied include the local field enhancement factor, the emission current density, the total emission current, the significant emission area, and the integrated field enhancement factor.
Field enhancement on the surface of an ellipsoidal field emitter. |  |
Selected Publications:
T Mueller, AG Kusne, R Ramprasad. "Machine learning in materials science: Recent progress and emerging applications." Rev. Comput. Chem.(Accepted for publication) (2015).AG Kusne, DN Lambeth. "Generalized analytical solution and study of conductive ellipsoidal field emitters." Electron Devices, IEEE Transactions on 57.3 (2010): 712-719.
AG Kusne, DN Lambeth. "Analytic Assessment of the Significant Emission Area and Integrated Enhancement Factor for Ellipsoidal Electron Field Emitters." Electron Devices, IEEE Transactions on 57.12 (2010): 3491-3499.