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Topological Data Analysis for Materials Science

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If you have a question about this talk, please contact Joseph Nelson.

It is a serious but exciting challenge to detect characteristic order or structural motifs buried in a complex system and convert them into quantitative descriptors useful for machine-learning. Topological data analysis (TDA) based on persistent homology (PH) is a powerful mathematical framework to treat such a problem. In this talk, we will see how TDA detects hidden mechanism and quantifies it from time evolution of complex amorphous carbon obtained by first-principles MD simulations at high temperature.

This talk will be held online using Zoom. Please register your email address here to receive Zoom links via email.

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This talk is part of the MSM-AIMR Joint Online Workshop 2020 series.

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