A Selective Multi-View Representation Augmentation Framework for Crystal Property Prediction

Abstract

Computational prediction of crystal properties plays a pivotal role in materials science. With the accelerated progress in machine learning, crystal property prediction has seen remarkable advancements. Nevertheless, the utilization of machine learning in this context faces several challenges. First, existing methods that utilize the smallest repeatable unit cell of a crystal often have a limited receptive field. Second, as experiments measuring crystal properties are time-consuming, labeled data is often scarce. To address these challenges, we propose a S elect I ve mu L ti- V iew representation A ugmentation framework (SILVA) for crystal property prediction. To go beyond limited receptive fields, we introduce the notion of a crystal supercell, which enables more comprehensive explorations of crystal structure. To fully combine insights from multi-view structures, i.e., from unit cells and supercells, we propose a multi-view representation learning (MRL) module that features a representation space that enhances the learning of representative features specific to different views. To alleviate the limited availability of labeled data, we propose a selective representation augmentation (SRA) module. Given representations of labeled training data, we carefully select nearby representations in the representation space established by the MRL module so that labels can be reused. An experimental study offers evidence that SILVA is capable of outperforming state-of-the-art methods.