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.

We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel. The deep learning model results in a mean disorientation error of 0.548° compared to 0.652° using dictionary based indexing.

With the ever-increasing need for wireless communication and the emergence of many systems, it is important to design broadband antennas to cover a wide frequency range. The aim of this paper is to design a broadband patch antenna, employing the three techniques of slotting, adding directly coupled parasitic elements and fractal electromagnetic band gap (EBG) structures.The bandwidth is improved from 9.3 to 23.7%. A wideband ranging from 4.15 to 5.27 GHz is obtained. Also, a comparative analysis of embedding EBG structures at different heights is also done. The composite effect of integrating these techniques in the design provides a simple and efficient method for obtaining low-profile, broadband, and high-gain antenna. By the addition of parasitic elements the bandwidth was increased to 18%. Later on by embedding EBG structures the bandwidth was increased up to 23.7%. The design is suitable for a variety of wireless applications like WLAN and radar applications.