Document Type
Article
Publication Date
2-20-2024
Abstract
Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.
Keywords
image analysis, particle growth kinetics, materials data science
Language
English
Publication Title
Integrating Materials and Manufacturing Innovation
Grant
DE-AC52-07NA27344
Rights
© The Author(s) 2024. This is an Open Access work distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Nalin Venkat, S., Ciardi, T.G., Lu, M. et al. A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences. Integr Mater Manuf Innov 13, 71–82 (2024). https://doi.org/10.1007/s40192-024-00342-w
Manuscript Version
Final Publisher Version