| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
|
| Presentation Title |
Predicting Compressive Strength of Consolidated Solids from Features Extracted from SEM Images |
| Author(s) |
T. Yong Han |
| On-Site Speaker (Planned) |
T. Yong Han |
| Abstract Scope |
Application of computer vision, machine learning, and deep learning in materials science can provide powerful tools to analyze and automate scientific data analysis. Here, we explored the application of computer vision and machine learning to quantify materials properties based on SEM images of materials microstructure. We showed that it is possible to train machine learning models to predict materials performance based on SEM images alone, demonstrating this capability on predicting uniaxially compressed peak stress of consolidated molecular solid samples. We explore two complementary approaches to this problem: (1) a traditional machine learning approach using state-of-the-art computer vision features and (2) an end-to-end deep learning approach, where features are learned automatically from raw images. We demonstrated that random forest performs best in the “small data” regime in which many real-world scientific applications reside, whereas deep learning outpaces random forest in the “big data” regime, where abundant training samples are available. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |