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Search Publications by: Aaron Gilad Kusne (Fed)

Displaying 51 - 63 of 63

Machine learning modeling of superconducting critical temperature

June 28, 2018

Author(s)

Aaron Gilad Kusne, Valentin Stanev, Ichiro Takeuchi

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical

Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics

October 12, 2017

Author(s)

Olivera Kotevska, Aaron Gilad Kusne, Daniel Samarov, Ahmed Lbath, Abdella Battou

Today's cities generate tremendous amounts of data thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context- aware services and systems, ensuring smart city services are

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

February 3, 2017

Author(s)

Aaron Gilad Kusne, Ichiro Takeuchi, Yuma Iwasaki

Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation, fabrication, and characterization. In particular, machine learning

Perspective: Compositionstructureproperty mapping in high-throughput experiments: Turning data into knowledge

May 26, 2016

Author(s)

Aaron G. Kusne, Jason Hattrick-Simpers, John Gregoire

With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and characterize

Machine learning in materials science: Recent progress and emerging applications

May 6, 2016

Author(s)

Aaron G. Kusne, Tim Mueller, Ramamurthy Ramprasad

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

October 15, 2015

Author(s)

Aaron G. Kusne

Advances in high-throughput materials fabrication and characterization techniques have resulted in faster rates of data collection and rapidly growing volumes of experimental data. To convert this mass of information into actionable knowledge of material

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

September 15, 2014

Author(s)

Aaron G. Kusne, Christian J. Long, Ichiro Takeuchi, Tieren Gao

Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning