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.
Highlights
In this era of big data, we are being bombarded with huge volumes of data from a variety of different sources at a staggering velocity in practically all fields of science and engineering, and materials science is no exception
Advanced techniques for datadriven analytics are needed to analyze these data in ways that can help extract meaningful information and knowledge from them, and contribute to accelerating materials discovery and realize the vision of Materials Genome Initiative (MGI).[1]
Illustrative examples of deep materials informatics that we review in this paper include learning the chemistry of materials using only elemental composition,[24] structure-aware property prediction,[25,26] crystal structure prediction,[27] learning multiscale homogenization[28,29] and localization[30] linkages in high-contrast composites, structure characterization[31,32] and quantification,[33,34] and microstructure reconstruction[35] and design.[36]
Summary
In this era of big data, we are being bombarded with huge volumes of data from a variety of different sources (experiments and simulations) at a staggering velocity in practically all fields of science and engineering, and materials science is no exception. A convolutional neural network (CNN) is a special kind of deep learning network which is designed to be used on spatial data such as images, and consists of three types of hidden layers.
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