The human brain provides a highly-efficient model for adaptive learning, information processing, and energy efficient computation. Unlike von Neumann computing architectures, where a central processor fetches data from a separate memory unit, neural elements use distributed, constantly evolving, and interacting weights stored across a dense, highly interconnected network. Efforts to replicate neuromorphic circuitry are reliant on the understanding of the essential materials concepts to achieve this goal, theoretical modeling and simulations of the mechanisms, discovery of new materials, and the design of novel architectures. The goal of this symposium is to bring together theoreticians and experimentalists seeking to elucidate fundamental materials design principles underpinning neuromorphic function with the emerging community of researchers interested in expanding the palette of materials exhibiting neuromorphic functionality. The symposium will focus on the mechanistic origins of memristive behavior spanning the range from atomistic understanding to mesoscale phenomena, encompassing state-of-the-art as well as emerging materials, with a strong emphasis on using simulations and theory to gain in-depth insight and predict new material properties/mechanisms. The symposium will comprise invited lectures from established technical leaders in the field as well as provide opportunities for early career researchers to present their research. The planned symposium will allow for development of a roadmap of this rapidly emerging area that holds promise for revolutionizing information processing.

electrical properties ferroelectricity metal-insulator transition nanoscale thin film