Recent advances and applications of deep learning methods in materials science

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. 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.

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• 1 DL allows analysis of unstructured data and automated identification of features.
• 2 The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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