Conan-ML software is a tool engineered for precise and automated processing and analysis of contact angle data. The software utilizes image processing algorithms to analyze captured images of droplets on surfaces. Current implementation is only optimised for high angle systems. More information about the method and accuracy is available here: https://doi.org/10.1021/acs.langmuir.4c01050
Conan-ML is written to run on python3.6+. The packages required to run conan ML must be installed in your Python environment. These are included listed in requirements.txt and can be install using
pip install -r requirements.txt
To load Conan-ML GUI run the conan.py file using
python conan.py
However, functions from each file of the modules directory can be called for a more customised approach.
This section has been included as many users of Conan-ML will not be familiar with the use of ML models and their limitations and best practice use cases.
The key limitation of ML models is that accuracy may deteriorate when used on systems which was not represented within it's training data. While it has been shown that the model can be applied to systems of contact angles below 110°, caution should be applied applied in these cases. It is recommended that contact angles are plotted and briefly examined (i.e. sense-checked) as general practice, but particularly for systems outside of training domain. Similarly, drops with Bond numbers greater than 2 were not included in the training set and should be approached with caution.
Surface roughness and reflection were included to train the model to ignore inputted data which is not the drop edge. However, few images with surface roughness which deviated from the training data were included in the experimental data set. As such users are again advised to check outputs for systems outside of the training range.
As the resolution of an image can be altered, should the resolution of an image be too high it will be lowered to give an input suitable for the ML model. This is the only exception to the above limitations.
High quality edge detection should be used to achieve the best results. This work presents an automated process, which still requires improvement, but will likely be suitable for high contrast images. Users are recommended to check that the detected edge is reasonable prior to accepting the results outputted by any fitting or angle prediction approach.