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Bootcamp: Machine Learning for Materials Research Workshop: Machine Learning Quantum Materials

Introduction to Python and Machine Learning

The event will introduce materials researchers from industry, national laboratories, and academia to machine learning theory and tools for rapid materials data analysis.



Three days of lectures and hands-on exercises covering a range of data analysis topics from data pre-processing through advanced machine learning analysis techniques. Example topics include:

•   Identifying important features in complex/high dimensional data

•   Visualizing high dimensional data to facilitate user analysis.

•   Identifying the fabrication ‘descriptors’ that best predict variance in functional properties.

•   Quantifying similarities between materials using complex/high dimensional data

The hands-on exercises will demonstrate practical use of machine learning tools on real materials data (scalar values, spectra, micrographs, etc.).

Demonstrations and Talks by Top Researchers

Confirmed Speakers:

Andrew Millis (Columbia)

Antoine Georges (CCQ)
Karin Rabe (Rutgers)
Sasha Balatsky (LANL)
Roger Melko (Waterloo)
Shoucheng Zhang (Stanford)

Mike Norman (ANL)

Computational Approaches

Stefano Curtarolo (Duke)
Gus Hart (BYU)
Giuseppe Carleo (Flatiron)
Miles Soudenmire (Flatiron)

Experimental Approaches
Jiun-Haw Chu (Univ. Washington)

Sergei Kalinin (ORNL)
Benji Maruyama (AFRL)

Ichiro Takeuchi (UMD)

Created July 13, 2018