| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Machine Learning and Image Processing Techniques for Materials Evaluation |
| Author(s) |
Roger H. French, Benjamin G. Pierce |
| On-Site Speaker (Planned) |
Roger H. French |
| Abstract Scope |
Statistical and machine learning techniques provide researchers with tools to evaluate and quantify materials’ performance. We describe a series of algorithms used to investigate photovoltaic cells via electroluminescence (EL) imaging and current-voltage (I-V) curves. Using convolutional neural networks (CNNs), we classify modules into groups based on which type and degree of degradation. An expansion of this idea uses data integration of IV features to produce predictive and inferential models of power and corrosion from EL images. However, these supervised learning models depend on prelabeled data; whereas our third machine learning algorithm, based on feature extraction and high-dimensional clustering, can mitigate this problem by sorting EL images based on the features detected. We also present a case study of large-scale image analysis of nucleation and growth of AlN crystals from an Al/Ni alloy using a high-performance, distributed computing approach, and the code packages and tooling involved in this image analysis. |