About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Materials
|
Presentation Title |
M-16: Building an ImageNet for Materials Grain Boundaries |
Author(s) |
Huolin Xin, Jose Arturo Venegas, Chengyun Zhao |
On-Site Speaker (Planned) |
Huolin Xin |
Abstract Scope |
Recent development in machine learning has nearly solved all computer vision problems that challenged the field in the past thirty years, however, most of these successful approaches are based on supervised learning meaning that ground truth labeling is needed for the training set. In this respect, the ImageNet project has become the cornerstone of the computer vision AI field. It shows the power of existing AI algorithms when labeled data are provided. In this work, we will present our effort to fill in the gap for the scientific imaging field. Specifically, we build a large library of scientific images where grain boundaries are present and the ground truth label are provided. We show that with this GrainBoundaryImageNet, we can build highly accurate models for providing inference for research on materials grain boundaries. This research is supported by the National Science Foundation through the UCI CCAM (DMR-2011967). |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Characterization, |