Inferring low-dimensional microstructure representations using convolutional neural networks.

Area of Science:

• Materials Informatics
• Computer Vision
• Machine Learning

Background:

• Statistical representation of microstructural images is crucial for materials science.
• Traditional methods like two-point correlations have limitations in capturing complex microstructural features.

Purpose of the Study:

• To develop an advanced method for the statistical representation of microstructural images using machine learning.
• To compare the effectiveness of a novel machine learning approach against traditional methods.

Main Methods:

• Utilized a pretrained convolutional neural network (CNN) to extract high-dimensional features from microstructural images.
• Applied manifold learning to reduce the dimensionality of the CNN-derived features, creating a low-dimensional embedding.
• Compared the embeddings generated by the CNN method with those from two-point correlation analysis.

Main Results:

• The low-dimensional embedding derived from the CNN accurately extracted the parameters used to generate synthetic microstructural images.
• The CNN-based method demonstrated significantly superior performance compared to the two-point correlation method across various evaluation metrics.
• This approach provides a more effective statistical characterization of microstructures.

Conclusions:

• Recent advances in machine learning, specifically CNNs, offer a powerful tool for materials informatics.
• The proposed method provides a superior statistical representation of microstructural images compared to conventional techniques.
• This work paves the way for more accurate and efficient analysis of material microstructures.