Recent Advances of Graphene and Related Materials in Artificial Intelligence

Abstract

Biological brains perform real‐time processing of unstructured data with ultralow energy consumption and represent the most efficient computing systems. Emulating the working principles of biological brains brings a revolutionary breakthrough in artificial intelligence (AI), generating two kinds of imitation approaches including machine learning (ML) and neuromorphic computing (NC) with artificial neurons/synapses. Herein this review, a general description of ML methods, the concept, and principle of artificial synapses (ASs) in NC derived from biological synapses, and their relationships, is provided. Graphene, one of the most representative 2D materials, arouses considerable attention due to its unique structure and properties. The application of ML in properties prediction (electronic properties, mechanical properties, thermal properties, cytotoxicity), structure recognition (atomic structure, microscopic dimensions/shapes), inverse design (composition, structure), and task recognition (chemical recognition, motion recognition, 3D imaging) of graphene and its derivatives and composites are summarized, and corresponding methods are discussed in the case studies. Recent progress in the development and application of graphene‐based ASs (synaptic transistors and memristors) is briefly introduced, where graphene‐based materials serve as the channel materials of synaptic transistors, the memristive materials, or back electrodes of synaptic memristors. Finally, the main challenges and prospects of graphene‐incorporated ML and ASs are presented.

Keywords

• artificial intelligence
• graphene
• machine learning
• neuromorphic computing
• synaptic devices