Materials Morphology Python Package
m2py: Materials Morphology Python Package
- Wesley Tatum, PhD Student at University of Washington MSE Department
- Diego Torrejon, Staff Machine Learning Engineer at BlackSky
- Patrick O’Neil, Director of Machine Learning and Artificial Intelligence at BlackSky
Clone or download this repository
pip install -r requirements.txt
pip install m2py
At the moment, we are offering two tutorials found under the Tutorials directory. There is an introductory tutorial showing the basic processing capabilities of m2py. The other tutorial is more advanced, showing how users can use current m2py capabilities to make them suitable for their own applications.
Thin films of semiconducting materials will enable stretchable and flexible electronic devices, but these thin films are currently stochastic and inconsistent in their properties and morphologies because processing and chemical conditions influence the mixing and domain size of the different components. By using atomic force microscopy (AFM), a cheap and quick technique, it is possible to spatially resolve and quantify these different domains based on differences in their mechanical properties, which are strongly correlated to their electronic performance. For this project, a library of AFM images has been curated, which includes poly(3- hexylthiophene) that has been processed in different ways (e.g. annealing time and temperature, thin film vs nanowire), as well as thin film mixtures of PTB7-th and PC 71 BM. To analyze these samples, several semantic segmentation methods from the fields of machine learning and topological data analysis are employed. Among these, a Gaussian mixture model utilizing machine learned local geometric features proved effective. From the segmentation, probability distributions describing the mechanical properties of each semantic segment can be obtained, allowing the accurate classification of the various phase domains present in each sample.
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