Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

作者：Grace Guinan, Addison Salvador, Michelle A. Smeaton, Andrew Glaws, Hilary Egan, Brian C. Wyatt, Babak Anasori, K Fiedler, Matthew J. Olszta, Steven R. Spurgeon · 发表于：APL Machine Learning · 年份：2025 · DOI：10.1063/5.0267699 · 被引用次数：4 · 研究领域：Machine Learning in Materials Science、Artificial Intelligence in Healthcare and Education、Cell Image Analysis Techniques

What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.