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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.


A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Now On-Demand Only: Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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