Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction
Machine Learning
Summary
The authors studied special polymers called OMIECs that are useful for advanced electronics and bioelectronics. They wanted to better understand how the structure of these polymers affects how well they conduct electrical charges. Using a technique called scanning nanodiffraction in a microscope, they gathered complex data that's hard to analyze. To improve this, the authors trained a machine learning model to identify important patterns in the data faster and more accurately than traditional methods, enabling quicker analysis during experiments.
Authors
Nicholas Marchese, Arthur R. C. McCray, Yael Tsarfati, Karen Bustillo, Adam Marks, Alberto Salleo, Colin Ophus
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. 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) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.