Microstructure Representation and Reconstruction of Heterogeneous
Materials via Deep Belief Network for Computational Material Design
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by
Ruijin Cang, Yaopengxiao Xu, Shaohua Chen, Yongming Liu, Yang Jiao,
Max Yi Ren
2016
Abstract
Integrated Computational Materials Engineering (ICME) aims to accelerate
optimal design of complex material systems by integrating material science and
design automation. For tractable ICME, it is required that (1) a structural
feature space be identified to allow reconstruction of new designs, and (2) the
reconstruction process be property-preserving. The majority of existing
structural presentation schemes rely on the designer's understanding of
specific material systems to identify geometric and statistical features, which
could be biased and insufficient for reconstructing physically meaningful
microstructures of complex material systems. In this paper, we develop a
feature learning mechanism based on convolutional deep belief network to
automate a two-way conversion between microstructures and their
lower-dimensional feature representations, and to achieves a 1000-fold
dimension reduction from the microstructure space. The proposed model is
applied to a wide spectrum of heterogeneous material systems with distinct
microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy,
Fontainebleau sandstone, and Spherical colloids, to produce material
reconstructions that are close to the original samples with respect to 2-point
correlation functions and mean critical fracture strength. This capability is
not achieved by existing synthesis methods that rely on the Markovian
assumption of material microstructures.
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