arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.
By Sheng Bi, Yi-Ze Wang, Jun Cheng
arXiv:2608. 09104v1 Announce Type: cross Abstract: Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.
By Aditya Raghavan, Yu Liu, Ian Mercer, JP Maria, Sergei Kalinin
arXiv:2606. 13859v1 Announce Type: cross Abstract: Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials applied during operation.
By Yu Liu, Stanislav Udovenko, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Susan Trolier-McKinstry, Sergei V. Kalinin
QuPAINT is a physics‑aware multimodal framework designed to characterize two‑dimensional quantum materials using optical microscopy. It employs the Synthetic Materials Framework (Synthia) to generate diverse synthetic images that preserve layer‑dependent optical behavior, and builds the QMat‑Instruct dataset with image‑specific reasoning traces. The framework integrates these signals through Physics‑Informed Attention (PIA) and is evaluated on the newly introduced QF‑Bench benchmark, achieving state‑of‑the‑art performance for flake detection and demonstrating improved spatial grounding and confidence calibration.
By Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu
arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.
By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia
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