Journal
COMPUTATIONAL MATERIALS SCIENCE
Volume 150, Issue -, Pages 212-221Publisher
ELSEVIER
DOI: 10.1016/j.commatsci.2018.03.074
Keywords
Structure-property mapping; Integrated computational material engineering; Deep learning; Generative models
Categories
Funding
- NSF CMMI [1651147]
- Arizona State University, Tempe, United States
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1651147] Funding Source: National Science Foundation
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Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces. However, statistical modeling of highly nonlinear mappings defined on high-dimensional microstructure spaces is known to be datademanding. Thus, the added value of such predictive models diminishes in common cases where material samples (in forms of 2D or 3D microstructures) become costly to acquire either experimentally or computationally. To this end, we propose a generative machine learning model that creates an arbitrary amount of artificial material samples with negligible computation cost, when trained on only a limited amount of authentic samples. The key contribution of this work is the introduction of a morphology constraint to the training of the generative model, that enforces the resultant artificial material samples to have the same morphology distribution as the authentic ones. We show empirically that the proposed model creates artificial samples that better match with the authentic ones in material property distributions than those generated from a state-of-theart Markov Random Field model, and thus is more effective at improving the prediction performance of a predictive structure-property model.
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