Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials

Abstract

The evolving field of Machine Learning (ML) and Artificial Intelligence (AI) contributed tremendously to the advancement of various branches of science and technology. Deep learning has attracted great interest from the research community of material science, because of its ability to statistically analyze a large collection of data. Along with the computational task, time efficient tools of machine learning have also been applied for the prediction of design and properties of new materials. A noticeable shift from trial and error-based laboratory approach to the modeling and simulation-based software techniques in the preparation and characterization of functional materials manifests the emergence of big data in the field of material science. The efficient algorithms enable data collection, storage with high security, fast processing, and interpretation of physically generated results. Embedding ML in material science research also provides distinctions between simulated data and experimental results. It has put the research of physical and chemical science at the forefront with the advancements in image processing, photonics, optoelectronics, and other emerging areas of material science. In this chapter, we review applications of machine learning algorithms to study experimentally obtained results of physical systems. A comprehensive study of different techniques of deep learning to design and predict new functional materials is detailed. We conclude with the discussion of future directions and challenges in the acceptability of this advanced technique to the existing vast area of material science.