From the abstract: Stress-strain curves are an important representation of a material’s mechanical properties, from which important properties such as elastic modulus, strength, and toughness, are deﬁned. However, generating stress-strain curves from numerical methods such as ﬁnite element method (FEM) is computationally intensive, especially when considering the entire failure path for a material. As a result, it is difﬁcult to perform high throughput com-putational design of materials with large design spaces, especially when considering mechanical responses be-yond the elastic limit. In this work, a combination of principal component analysis (PCA) and convolutional neural networks (CNN) are used to predict the entire stress-strain behavior of binary composites evaluated over the entire failure path, motivated by the signiﬁcantly faster inference speed of empirical models. We show that PCA transforms the stress-strain curves into an effective latent space by visualizing the eigenbasis of PCA. Despite having a dataset of only 10-27% of possible microstructure conﬁgurations, the mean absolute error of the prediction is b10% of the range of values in the dataset, when measuring model performance based on de-rived material descriptors, such as modulus, strength, and toughness. Our study demonstrates the potential to use machine learning to accelerate material design, characterization, and optimization.