Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910968356798464 |
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| author | Guinan, Grace Salvador, Addison Smeaton, Michelle A. Glaws, Andrew Egan, Hilary Wyatt, Brian C. Anasori, Babak Fiedler, Kevin R. Olszta, Matthew J. Spurgeon, Steven R. |
| author_facet | Guinan, Grace Salvador, Addison Smeaton, Michelle A. Glaws, Andrew Egan, Hilary Wyatt, Brian C. Anasori, Babak Fiedler, Kevin R. Olszta, Matthew J. Spurgeon, Steven R. |
| contents | What does materials science look like in the "Age of Artificial Intelligence?" Each materials 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization Guinan, Grace Salvador, Addison Smeaton, Michelle A. Glaws, Andrew Egan, Hilary Wyatt, Brian C. Anasori, Babak Fiedler, Kevin R. Olszta, Matthew J. Spurgeon, Steven R. Materials Science Artificial Intelligence What does materials science look like in the "Age of Artificial Intelligence?" Each materials 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. |
| title | Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2502.18604 |