arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.
By Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh
arXiv:2609.14348v1 Announce Type: cross
Abstract: Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations wit...
By Haoran Zhang, Zian Mao, Shufen Chu, Xiaoya He, Yuyan Guan, Antong Yang, Mingze Li, Xiaoqin Zeng, Yujun Xie
arXiv:2607. 16570v1 Announce Type: cross Abstract: Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics.
By Nicholas Marchese, Arthur R. C. McCray, Yael Tsarfati, Karen Bustillo, Adam Marks, Alberto Salleo, Colin Ophus
arXiv:2609.36639v1 Announce Type: new
Abstract: Scanning Tunneling Microscopy (STM) is a widely used tool for characterizing surfaces of materials at the atomic scale, playing a crucial role in disco...
By Huanhuan Zhao, Laxmi Bhurtel, Connor Vernachio, Fahmy Paiziah, Wonhee Ko, Arpan Biswas
The paper introduces a ptychography‑supervised learning framework that transforms 4D‑STEM data into real‑time atomic‑resolution phase images. By training a compact model on physics‑constrained reference phase maps from a single AuPd dataset, the method predicts local phase patches directly from diffraction patterns without probe calibration or iterative optimization. The resulting workflow achieves an online latency of ~0.27 ms per probe position, a 1,000‑fold speed‑up over GPU‑accelerated ePIE, and maintains atomic‑scale lattice contrast while generalizing across materials, defocus conditions, and instruments.
By H. Yue, C. -C. Chen, C. -N. Hsiao, J. Cheng, Y. Liu, X. Z. Liao, Steve F. Shu
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.
By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban