Artificial Intelligence and Machine Learning in Advanced Materials Science

Abstract

The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smart production of polymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials.

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