Predicting elastic and plastic properties of small iron polycrystals by machine learning

Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study...

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Bibliographic Details
Published in:arXiv.org 2023-08
Main Authors: Mińkowski, Marcin, Laurson, Lasse
Format: Article
Language:English
Subjects:
Artificial neural networks
Complex systems
Elastic deformation
Elastic properties
Iron
Machine learning
Mechanical properties
Molecular dynamics
Perturbation
Plastic properties
Polycrystals
Shear modulus
Stress-strain curves
Tessellation
Yield strength
Yield stress
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Summary:Deformation of crystalline materials is an interesting example of complex system behaviour. Small samples typically exhibit a stochastic-like, irregular response to externally applied stresses, manifested as significant sample-to-sample variation in their mechanical properties. In this work we study the predictability of the sample-dependent shear moduli and yield stresses of a large set of small cube-shaped iron polycrystals generated by Voronoi tesselation, by combining molecular dynamics simulations and machine learning. Training a convolutional neural network to infer the mapping between the initial polycrystalline structure of the samples and features of the ensuing stress-strain curves reveals that the shear modulus can be predicted better than the yield stress. We discuss our results in the context of the sensitivity of the system's response to small perturbations of its initial state.
ISSN:2331-8422
DOI:10.48550/arxiv.2302.13745