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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.

 Combinatorial Library to Structural Phase Response Diagram
Discovery of structure phase diagram from composition spread data.

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.
Field enhancement on the surface of an ellipsoidal field emitter. 

Selected Publications:

Kusne, A. G., et al. "High-throughput determination of structural phase diagram and constituent phases using GRENDEL." Nanotechnology 26.44 (2015): 444002.

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, et al. "On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets." Scientific reports 4 (2014).

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.


Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in
Created April 7, 2019