The length and time scales of material structures and phenomena vary significantly among these four elements, adding further complexity 2. “Processing-structure-property-performance” is the key mantra in Materials Science and Engineering (MSE) 1. 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.

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.

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. 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. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. DL allows analysis of unstructured data and automated identification of features. 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.

Machine learning deep learning