Deep materials informatics: Applications of deep learning in materials science

Research output: Contribution to journalReview articlepeer-review

151 Scopus citations


The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.

Original languageEnglish (US)
Pages (from-to)779-792
Number of pages14
JournalMRS Communications
Issue number3
StatePublished - Sep 1 2019

ASJC Scopus subject areas

  • General Materials Science


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