Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction
Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML)...
| Authors | Nicholas Marchese, Arthur R. C. McCray, Yael Tsarfati et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.