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Contrast Transfer Functions Help Quantify Neural Network Out-Of-Distribution Generalization in HRTEM
Luis Rangel DaCosta1,2, Mary C Scott1,2
1Department of Materials Science and Engineering, University of California, Hearst Memorial Mining Building, Berkeley, CA 94720, USA.
Summary
Neural networks show stable performance in high-resolution transmission electron microscopy (HRTEM) segmentation tasks. However, their accuracy predictably declines as imaging conditions deviate from the training data distribution.
Area of Science:
- Materials Science
- Computational Science
- Machine Learning
Background:
- Neural networks excel at scientific tasks but struggle with out-of-distribution (OOD) generalization.
- Understanding OOD generalization is crucial for deploying AI in experimental settings with variable conditions.
Purpose of the Study:
- To investigate the OOD generalization of neural network segmentation models in high-resolution transmission electron microscopy (HRTEM).
- To analyze the impact of varying imaging conditions on model performance using synthetic data.
Main Methods:
- Trained and evaluated over 12,000 neural networks on synthetic HRTEM data.
- Utilized random structure sampling and multislice simulation for data generation.
- Developed a framework using the HRTEM contrast transfer function to quantify OOD domain shifts.
Main Results:
- Neural network segmentation models demonstrated significant performance stability.
- Model performance degraded smoothly and predictably as imaging conditions shifted away from the training distribution.
- Quantified OOD domain shifts based on imaging condition variations.
Conclusions:
- Neural network segmentation models exhibit predictable OOD generalization behavior concerning imaging conditions in HRTEM.
- The study provides a framework for assessing OOD shifts in imaging data.
- Acknowledges limitations in explaining OOD shifts related to atomic structures and suggests complementary approaches.
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