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.

翻译：晶体材料具有对称且周期性的结构，展现出广泛的性质谱系，并已在电子、能源等诸多领域得到广泛应用。对于晶体材料的发现，传统的实验与计算方法耗时且昂贵。近年来，得益于晶体材料数据的爆炸式增长，数据驱动的材料发现受到了极大关注。特别是，最新的进展利用深度学习的强大表征能力来建模晶体材料中高度复杂的原子系统，为快速、准确的材料发现开辟了新途径。这些工作通常聚焦于四类任务，包括物理化学性质预测、晶体材料合成、辅助表征以及加速理论计算。尽管取得了显著进展，目前仍缺乏系统性的研究来总结其区别与局限。为填补这一空白，我们系统性地调研了近年来的进展。我们首先介绍了晶体材料的几种数据表示方法。基于这些表示，我们总结了各类基础深度学习模型及其在不同材料发现任务中的针对性应用。最后，我们指出了当前面临的挑战并提出了未来发展方向。