  DeepAngle is a machine learning-based method to determine the contact angles
of different phases in the tomography images of porous materials. Measurement
of angles in 3--D needs to be done within the surface perpendicular to the
angle planes, and it could become inaccurate when dealing with the discretized
space of the image voxels. A computationally intensive solution is to correlate
and vectorize all surfaces using an adaptable grid, and then measure the angles
within the desired planes. On the contrary, the present study provides a rapid
and low-cost technique powered by deep learning to estimate the interfacial
angles directly from images. DeepAngle is tested on both synthetic and
realistic images against the direct measurement technique and found to improve
the r-squared by 5 to 16% while lowering the computational cost 20 times. This
rapid method is especially applicable for processing large tomography data and
time-resolved images, which is computationally intensive. The developed code
and the dataset are available at an open repository on GitHub
(https://www.github.com/ArashRabbani/DeepAngle).
