Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN

Abstract

Despite its simplicity, the composition of a material can be used as input to machine learning models to predict a range of materials properties. However, many property optimization tasks require the generation of novel but realistic materials compositions. In this study, we describe a way to generate compositions of hybrid organic–inorganic crystals through adapting Augmented CycleGAN, a novel generative model that can learn many-to-many relations between two domains. Specifically, we investigate the problem of composition change upon amine swap: for a specific chemical system (set of elements) crystalized with amine A, how would the product chemical compositions change if it is crystalized with amine B? By training with limited data from Cambridge Structural Database, our model can generate realistic chemical compositions for hybrid crystalline materials. The Augmented CycleGAN model can also utilize abundant unpaired data (compositions of different chemical systems), a feature that traditional supervised methods lack. The generated compositions can be used for many tasks, for example, as input fed to a classifier that predicts structural dimensionality.