AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI
- 1Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , TN 37830 , USA
Abstract
The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ensure quality (such as in the semiconductor industry), and highly customized, operator-driven systems requiring human experts. The former offers high throughput but limited flexibility, whereas the latter is heavily bandwidth-limited but provides research-grade discovery capabilities. Recent advances in "self-driving" characterization tools offer the potential to bridge the two stratified regimes, by the creation of application program interfaces (APIs) that can control hardware, and the integration of AI methods to incorporate autonomy into the process. Here, we discuss our latest developments in AEcroscopyWave, a custom-built characterization platform for the agentic-AI era, that provides unified control of scanning probe microscopes with programmable peripheral instrumentation, highlighting the design choices that are necessary for maximizing the capability of the system and the ease of use for both human and AI agents. The benefits of making heterogeneous scientific instruments accessible, composable and usable by agents is demonstrated by test cases.
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BibTeX
@misc{liu2026aecroscopywave,
title = {{AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI}},
author = {Yongtao Liu and Jawad Chowdhury and Ganesh Narasimha and Ralph Bulanadi and Liam Collins and Rubén Millán‐Solsona and Martí Checa and Asraful Haque and Sumner B. Harris and Stephen Jesse and Rama Vasudevan},
howpublished = {arXiv (Cornell University)},
year = {2026},
month = jul,
doi = {10.48550/arxiv.2607.22975},
url = {https://arxiv.org/abs/2607.22975},
note = {CC BY},
}
RIS
TY - UNPB
TI - AEcroscopyWave: Towards Self-Driving Characterization Platforms for Agentic AI
AU - Yongtao Liu
AU - Jawad Chowdhury
AU - Ganesh Narasimha
AU - Ralph Bulanadi
AU - Liam Collins
AU - Rubén Millán‐Solsona
AU - Martí Checa
AU - Asraful Haque
AU - Sumner B. Harris
AU - Stephen Jesse
AU - Rama Vasudevan
PY - 2026
DA - 2026/07/25
PB - arXiv (Cornell University)
DO - 10.48550/arxiv.2607.22975
UR - https://arxiv.org/abs/2607.22975
AB - The characterization of electronic materials has traditionally been stratified into two distinct regimens: industry-scale automated systems to inspect materials for defects and ensure quality (such as in the semiconductor industry), and highly customized, operator-driven systems requiring human experts. The former offers high throughput but limited flexibility, whereas the latter is heavily bandwidth-limited but provides research-grade discovery capabilities. Recent advances in "self-driving" characterization tools offer the potential to bridge the two stratified regimes, by the creation of application program interfaces (APIs) that can control hardware, and the integration of AI methods to incorporate autonomy into the process. Here, we discuss our latest developments in AEcroscopyWave, a custom-built characterization platform for the agentic-AI era, that provides unified control of scanning probe microscopes with programmable peripheral instrumentation, highlighting the design choices that are necessary for maximizing the capability of the system and the ease of use for both human and AI agents. The benefits of making heterogeneous scientific instruments accessible, composable and usable by agents is demonstrated by test cases.
SN - 2331-8422
ER -