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Machine Learning Techniques for Alloy Modeling
Presented by Fei Zhou, Lawrence Livermore National Laboratory

The recent advances within the field of machine learning algorithms are now starting to become ubiquitous in materials research. Dr. Zhou will review some recent developments and the application of ML tools towards alloy modeling. At the atomistic level, the automated construction of the complex potential energy surface and calculation of thermodynamic and transport properties using compressive sensing algorithms will be discussed. A brief introduction of the compressive sensing lattice dynamics (CSLD) and vibrational cluster expansion (VCE) codes will be given. He will also present some of the recent progress in image recognition and processing of experimental micrograph images for alloy solidification.

This is a public webinar.

May 29, 2019 12:00 PM in Mountain Time (US and Canada)

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Fei Zhou
Dr. Zhou earned his Ph.D. in Physics from MIT and has been a physicist at Lawrence Livermore National Laboratory since 2013. His primary research interests are in computational materials physics and materials informatics. His specialties include solid state physics, statistical mechanics, energy storage materials, lithium-ion batteries, data mining, and Mathematica programing.