This symposium pursues fundamental understanding of materials that will enable energy-efficient, massively parallel, and environment-tolerant computing, sensing, and data storage. The recent explosion of new ideas for neuromorphic, reservoir, thermal, and optical computing raises new opportunities as well as requirements for materials and devices. We seek to connect diverse audiences from academia, industry, and national labs with expertise in computing, materials theory and design, device measurements, advanced characterization, and AI/ML to outline the fundamental mechanisms of energy and information transduction in material systems, open questions and challenges in computing materials and architectures, and the application areas where new computing will find unique applications complementary to existing technologies. Particular attention will be drawn to synergistic approaches among synthesis, theory, modeling, and characterization that can bridge the multiplicity of energy, time, and length-scales involved in computation and enable accelerated adoption of new material systems for future information technologies.

Proposed sessions

  • Phase transitions in 2D materials and devices
  • CMOS compatible ferroelectric materials and devices
  • Fundamental materials needs for emerging computing paradigms
  • Electrochemical and reservoir memory and computing devices
  • Scalable modeling and computation for switching phenomena
  • Applications of machine learning in device modeling and characterization
  • Co-design of materials, devices, and systems
  • Advanced characterization techniques

 Organizers

Petro Maksymovych, Oak Ridge National Laboratory, USA, maksymovychp@ornl.gov

Yiyang Li, University of Michigan, USA

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