Inverse design of anisotropic spinodoid materials with prescribed diffusivities

Dataset and code used in M Röding, et al, "Inverse design of anisotropic spinodoid materials with prescribed diffusivity", published in Scientific Reports. In this work, we develop a framework for inverse design of a class of anisotropic materials with spinodoid i.e. spinodal decomposition-like morphology, where the structure is optimized to have prescribed diffusivity. We use a convolutional neural network (CNN) for predicting effective diffusivity in all three directions. The CNN is used in an approximate Bayesian computation (ABC)-based formulation of the inverse problem. Herein, the codes in Matlab and Python/Tensorflow for structure generation, prediction and inverse design are supplied, together with the dataset and the trained CNN model. The parts that require the proprietary code for lattice Boltzmann simulations of diffusion are not included.