STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
arXiv:2606.29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose. We introduce STEMGym, an open-source Gymnasium benchmark of 15 physics-simulated STEM worlds spanning five materials, three difficulty levels, and four characterisation tasks, score
The accelerating pace of AI development and the push for automation in scientific discovery are driving the need for more efficient and robust tools for autonomous experimentation.
This development challenges a core assumption in autonomous scientific imaging, suggesting that current approaches to 'smarter navigation' may not be the primary bottleneck, redirecting research efforts.
The focus of autonomous imaging research may shift from purely navigational AI to more fundamental materials science and dose management strategies, particularly in sensitive modalities like STEM.
- · Materials science researchers
- · Developers of custom sensor hardware for microscopy
- · Companies specializing in advanced microscopy techniques
- · Purely software-centric AI navigation companies in microscopy
- · Traditional, manual electron microscopy workflows
Research into autonomous electron microscopy will pivot to prioritize dose optimization strategies over complex navigation algorithms.
This shift could accelerate the development of new materials and imaging techniques that are inherently more resistant to electron dose damage.
The broader scientific community might re-evaluate assumptions about AI's most impactful role in other autonomous experimental setups, emphasizing fundamental physics over 'smart' heuristics.
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Read at arXiv cs.LG