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Session on Machine Learning for Disordered Mater ... (No replies)

3 months ago
Bauchy 3 months ago

Dear colleagues,

We would like to bring your attention to the session "Data-based Modeling and Machine Learning for Glass Science" that we are organizing at the GOMD conference (American Ceramic Society) on May 17-21, 2020 in New Orleans, LA:

The (already extended and final) deadline for abstract submission is December 11, 2019.

More details about the symposium:

Data-driven modeling and machine learning have been attracting a lot of attention in recent years to solve complex problems in the field of glass science. Specifically, machine learning methods have been demonstrated as promising tools to tackle open problems such as predicting composition–property relationships in glasses. The aim of this session is to focus on recent advances in the field of glass science achieved using data-driven modeling and machine learning. Topics of interest include, but are not restricted to, the use of data-based modeling to develop composition–property relationships, design optimized glass compositions, develop interatomic potentials, understand the fundamentals of glassy state for image processing, predict the structure of glasses, and develop empirical relationships.

Please let me know if you need more information. We look forward to seeing you in New Orleans!

Best regards,
Adama Tandia, Corning Inc., USA
Mathieu Bauchy, University of California Los Angeles, USA
N.M. Anoop Krishan, Indian Institute of Technology Delhi, India

Mathieu Bauchy, Ph.D.
Assistant Professor
University of California, Los Angeles (UCLA)
Office: 5731E Boelter Hall — (310) 825-9991
[email protected] || Google Scholar || PARISlab Laboratory
Core faculty: Institute for Carbon Management

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Ab initio (from electronic structure) calculation of complex processes in materials