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Neural Network Analysis of Dynamic Fracture in a Layered Material

Published online by Cambridge University Press:  02 January 2019

Pankaj Rajak
Affiliation:
Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, IL60439, USA; Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089
Rajiv K. Kalia
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089 Department of Physics, University of Southern California, Los Angeles, CA90089 Department of Computer Science, University of Southern California, Los Angeles, CA90089 Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089
Aiichiro Nakano
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089 Department of Physics, University of Southern California, Los Angeles, CA90089 Department of Computer Science, University of Southern California, Los Angeles, CA90089 Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089 Department of Biological Sciences, University of Southern California, Los Angeles, CA90089
Priya Vashishta
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089 Department of Physics, University of Southern California, Los Angeles, CA90089 Department of Computer Science, University of Southern California, Los Angeles, CA90089 Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089
Corresponding
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Abstract

Dynamic fracture of a two-dimensional MoWSe2 membrane is studied with molecular dynamics (MD) simulation. The system consists of a random distribution of WSe2 patches in a pre-cracked matrix of MoSe2. Under strain, the system shows toughening due to crack branching, crack closure and strain-induced structural phase transformation from 2H to 1T crystal structures. Different structures generated during MD simulation are analyzed using a three-layer, feed-forward neural network (NN) model. A training data set of 36,000 atoms is created where each atom is represented by a 50-dimension feature vector consisting of radial and angular symmetry functions. Hyper parameters of the symmetry functions and network architecture are tuned to minimize model complexity with high predictive power using feature learning, which shows an increase in model accuracy from 67% to 95%. The NN model classifies each atom in one of the six phases which are either as transition metal or chalcogen atoms in 2H phase, 1T phase and defects. Further t-SNE analyses of learned representation of these phases in the hidden layers of the NN model show that separation of all phases become clearer in the third layer than in layers 1 and 2.

Type
Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Materials Research Society 2018

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