Fig. 1: Inverse materials design workflow.
From: Inverse design of two-dimensional materials with invertible neural networks
Starting with a specified design space, training data is obtained with DFT which is then fed to train the MatDesINNe framework using invertible neural networks. Samples are generated, down-selected and localized to ensure high quality candidates. An optional validation step with DFT ensures the candidates have the intended target properties.
