Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

深度学习 (DL) 是材料数据科学中发展最快的主题之一，其应用迅速涌现，涵盖原子、基于图像、光谱和文本数据模式。DL 允许分析非结构化数据和自动识别特征。大型材料数据库的最新发展推动了深度学习方法在原子预测中的应用。相比之下，图像和光谱数据的进步在很大程度上利用了高质量前向模型以及生成无监督 DL 方法支持的合成数据。在本文中，我们对深度学习方法进行了高级概述，然后详细讨论了深度学习在原子模拟、材料成像、光谱分析和自然语言处理方面的最新发展。对于每种模式，我们讨论了涉及理论和实验数据的应用、典型的建模方法及其优势和局限性，以及相关的公开可用的软件和数据集。最后，我们讨论了与该领域不确定性量化相关的最新交叉工作，并简要介绍了材料科学中 DL 方法的局限性、挑战和潜在增长领域。