Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices

2025-01-07

AI Summary

To address the time-consuming, error-prone, and annotation-dependent nature of manual grain boundary identification in scanning electron microscopy (SEM) images of nanoparticle superlattices, this work proposes an unsupervised grain boundary detection and segmentation method. Innovatively integrating Radon transform with agglomerative hierarchical clustering, the approach maps pixel-level SEM images to interpretable numerical representations of grain orientation—eliminating reliance on labeled data entirely. The method jointly incorporates directional feature extraction, signal enhancement, and unsupervised modeling, ensuring high robustness under challenging conditions such as strong noise and ambiguous boundaries. It achieves a processing speed of four images per minute, enabling large-scale dataset analysis. Experimental validation demonstrates precise quantification of grain size and orientation distributions across varying temperature–pressure conditions. This provides a scalable, interpretable computational foundation for microstructure–property relationship modeling and materials process optimization.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes.

Problem

• Automatic Grain Boundary Detection
• Scanning Electron Microscope Images
• Material Property Analysis

Innovation

• Unsupervised Learning
• Signal Processing
• Automated Grain Segmentation