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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title The Composition-microstructure-property Relationship by Machine Learning
Author(s) Zongrui Pei, Michael C. Gao, Kyle Rozman, Tao Liu, David Alman, Jeffrey A. Hawk
On-Site Speaker (Planned) Zongrui Pei
Abstract Scope We present our latest proceeding of machine learning microstructure images of 9-12Cr martensitic/ferritic steels. The variational autoencoder (VAE) models are used to extract the features of Scanning Electron Microscopy (SEM) images. The goal of this study is two folds: (i) prediction of mechanical properties for given images for a type of alloy microstructure; (ii) generation of the microstructure for alloys given their compositions and heat treatment conditions. The two sub-aims are of great importance in design of novel materials. Once realized, the materials design process can be guided by machine learning algorithms. This will render the design process not only more reliable but more efficient as well. In this talk, we will present the machine-learned relation between composition and microstructures, and the relation between microstructures and yield stresses in 2D latent space. These pictures, offered by the VAE models, allow for straightforward demonstrations of the complex relationships among composition-microstructure-property.


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The Composition-microstructure-property Relationship by Machine Learning

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