Year: 2019
Type: article
Abstract: 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.
Source: MRS Communications
DOI: 10.1557/mrc.2019.73
Authors: Ankit Agrawal, Alok Choudhary
Institution: Northwestern University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,980
Awards: G1523888516, G2222204877, SCALABLE, IN‐SITU DATA CLUSTERING DATA ANALYSIS FOR EXTREME SCALE SCIENTIFIC COMPUTING, SHF: Medium: Collaborative Research: Scalable Algorithms for Spatio-temporal Data Analysis, G6071252945, ADVANCED MATERIALS CENTER OF EXCELLENCE, ADVANCED MATERIALS CENTER FOR EXCELLENCE: CENTER FOR HIERARCHICAL MATERIALS DESIGN (CHIMAD), G7830320759, SCALABLE DATA MANAGEMENT, ANALYSIS AND VISUALIZATION (SDAV) INSTITUTE
OpenAlex ID: W2953053221