Crystalline Material Discovery in the Era of Artificial Intelligence

TL;DR

This paper reviews recent advances in AI-driven crystalline material discovery, focusing on data representations, deep learning models, and their applications in predicting properties, synthesis, characterization, and computational acceleration.

Contribution

It provides a systematic summary of recent deep learning methods and data representations used in crystalline materials discovery, highlighting challenges and future directions.

Findings

Deep learning models effectively predict material properties.

Various data representations enable better modeling of atomic structures.

Identified challenges include data quality and model interpretability.

Abstract

Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond. For crystalline materials discovery, traditional experimental and computational approaches are time-consuming and expensive. In these years, thanks to the explosive amount of crystalline materials data, great interest has been given to data-driven materials discovery. Particularly, recent advancements have exploited the expressive representation ability of deep learning to model the highly complex atomic systems within crystalline materials, opening up new avenues for fast and accurate materials discovery. These works typically focus on four types of tasks, including physicochemical property prediction, crystalline material synthesis, aiding characterization, and accelerating theoretical computations. Despite the remarkable progress, there is still a lack of systematic investigation to summarize their distinctions and limitations. To fill this gap, we systematically investigated the progress made in recent years. We first introduce several data representations of the crystalline materials. Based on the representations, we summarize various fundamental deep learning models and their tailored usages in various material discovery tasks. Finally, we highlight the remaining challenges and propose future directions.