A multitude of convolution architectures with varying complexity have been proposed in recent years. While f conv in Equation ( ) could in principle be based on a variety of these methodologies, given our small training dataset, we utilize a low parameter complexity CGCNN convolution function for practical model training purposes. More details of the CGCNN convolutional function and the small modifications used herein are provided in Supplementary Section 4.