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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions.

Xiaolin Li¹, Yichi Zhang², He Zhao²

¹Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208, USA.

Scientific Reports

|September 9, 2018

Summary

This study introduces a versatile transfer learning method for reconstructing material microstructures and predicting their properties. This approach offers a general solution for diverse material systems, accelerating new material discovery.

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Area of Science:

Computational Materials Science
Artificial Intelligence in Materials Science

Background:

Stochastic microstructure reconstruction methods are often material-specific.
A generalizable approach is needed for broader applications in computational materials science.

Purpose of the Study:

To develop a transfer learning-based approach for microstructure reconstruction and structure-property predictions applicable to various material systems.
To provide a general and efficient solution for complex microstructure analysis.

Main Methods:

Utilized a deep convolutional neural network with an encoder-decoder architecture.
Implemented feature-matching optimization and model pruning for analysis.
Leveraged pruning insights for developing structure-property predictive models.

Main Results:

Demonstrated numerical generality across diverse material microstructures with complex geometrical features.
Successfully correlated microstructural features with hierarchical layers in the deep convolutional network.
Developed an effective structure-property predictive model.

Conclusions:

The proposed transfer learning approach offers a general, off-the-shelf solution for microstructure reconstruction and property prediction.
This method reduces the need for extensive prior knowledge and hyper-parameter tuning.
The approach has the potential to significantly expedite the discovery of novel materials.