Towards Self-Optimizing Electron Microscope: Robust Tuning of Aberration Coefficients via Physics-Aware Multi-Objective Bayesian Optimization

2026-01-26

AI Summary

This work addresses the limitations of conventional tuning methods in high-throughput aberration-corrected scanning transmission electron microscopy (STEM), which suffer from low sample efficiency and difficulty in jointly optimizing coupled aberration parameters, as well as the restricted generalizability of existing deep learning approaches. The authors propose a physics-aware multi-objective Bayesian optimization framework that integrates Gaussian process regression, user-defined physics-driven objective functions, and Pareto front analysis to efficiently explore the aberration space within an active learning loop. This method adapts seamlessly to varying sample conditions without retraining and demonstrates superior performance over traditional algorithms in correcting focus, astigmatism, and higher-order aberrations. It achieves enhanced robustness and data efficiency, thereby advancing electron microscopy toward a self-optimizing operational paradigm.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

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• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
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Abstract

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical column. While automated alignment routines exist, conventional approaches rely on serial, gradient-free searches (e.g., Nelder-Mead) that are sample-inefficient and struggle to correct multiple interacting parameters simultaneously. Conversely, emerging deep learning methods offer speed but often lack the flexibility to adapt to varying sample conditions without extensive retraining. Here, we introduce a Multi-Objective Bayesian Optimization (MOBO) framework for rapid, data-efficient aberration correction. Importantly, this framework does not prescribe a single notion of image quality; instead, it enables user-defined, physically motivated reward formulations (e.g., symmetry-induced objectives) and uses Pareto fronts to expose the resulting trade-offs between competing experimental priorities. By using Gaussian Process regression to model the aberration landscape probabilistically, our workflow actively selects the most informative lens settings to evaluate next, rather than performing an exhaustive blind search. We demonstrate that this active learning loop is more robust than traditional optimization algorithms and effectively tunes focus, astigmatism, and higher-order aberrations. By balancing competing objectives, this approach enables"self-optimizing"microscopy by dynamically sustaining optimal performance during experiments.

Problem

• aberration correction
• Scanning Transmission Electron Microscopy
• multi-objective optimization
• optical drift
• high-throughput microscopy

Innovation

• Multi-Objective Bayesian Optimization
• Aberration Correction
• Physics-Aware Optimization
• Active Learning
• Scanning Transmission Electron Microscopy