Modeling the relationship between mechanical yield stress and material geometry using convolutional neural networks

Abstract

Machine learning methods can be used to predict the properties of materials from their structure. This can be particularly useful in cases where other standard methods for finding material properties are time and resources consuming to use on large sample spaces. In this work, we study the strength of α-quartz crystals with a porous layer created by simplex noise as the shape of porosity. We train a neural network to predict the yield stress of these systems under both shear and tensile deformation. Molecular dynamics simulations are used for a randomly selected sample of possible structures in order to generate the ground truth to be used as the training data. We employ deep convolutional neural networks (CNNs) which are commonly used when dealing with image or image-like data since the input data for the problem in hand are a binary 2D structure of the porous layer of the systems. The trained CNN can predict the yield stress of a system based on the geometry of that given system, with much higher precision compared to a baseline polynomial regression method. Saliency maps created with the trained model show that the model predictions are most sensitive to altering structures near high-stress regions, indicating that the model makes predictions based on reasonable physics.