| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
|
| Presentation Title |
Deep Learning and Uncertainty Quantification for Automated Experiments |
| Author(s) |
Bobby Sumpter, Ayana Ghosh, Maxim Ziatdinov, Sergei Kalinin, Ondrej Dyck |
| On-Site Speaker (Planned) |
Bobby Sumpter |
| Abstract Scope |
In experimental imaging, rapid feature extraction is critical for conversion of the data streams to spatial or spatiotemporal arrays of features of interest. Deep learning while a powerful approach for feature extraction is often limited by the out-of-distribution drift between experiments, where the network trained for one set of conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated imaging experiment since retraining or transfer learning becomes impractical. To address this gap, we have recently explored the reproducibility of the deep learning for feature extraction in atom-resolved electron microscopy and demonstrated workflows based on ensemble learning and iterative training that greatly improve feature detection. This approach also enables incorporating uncertainty quantification into the deep learning analysis and rapid automated experimental workflows. In this talk I will present a summary of our recent work. |