Recent Advances and Applications of Deep Learning Methods in Materials Science

TL;DR

This paper reviews recent advances in deep learning applications across various data modalities in materials science, highlighting developments, challenges, and future prospects for materials discovery.

Contribution

It provides a comprehensive overview of deep learning methods in materials science, covering atomistic, imaging, spectral, and language data, with discussions on applications, datasets, and software.

Findings

Deep learning enhances analysis of unstructured materials data.

Synthetic data and generative models improve imaging and spectral analysis.

Uncertainty quantification is emerging as a key focus in the field.

Abstract

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. 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. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.