Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C)


Ivan Schuller

Lead Institution

University of California, San Diego




To lay down the quantum-materials-based foundation for the development of an energy-efficient, fault-tolerant computer that is inspired and works like the brain (“neuromorphic”).

Research Topics

bio-inspired, charge transport, magnetism and spin physics, quantum information science, mesoscale science, materials and chemistry by design, mesostructured materials, synthesis (novel materials), synthesis (predictive)

Materials Studied

Materials: metal, oxide, ceramic

Interfaces: inorganic/inorganic, solid/solid

Nanostructured Materials: 1D, 2D

Experimental and Theoretical Methods

X-ray diffraction and scattering, X-ray imaging, X-ray spectroscopy, electron microscopy, scanning probe microscopy, surface science, molecular dynamics (MD), density functional theory (DFT), dynamical mean field theory (DMFT), mesoscale modeling, multiscale modeling

Partner Institutions

  • Argonne National Laboratory
  • Brookhaven National Laboratory
  • National Center for Scientific Research
  • National Institute of Standards and Technology
  • New York University
  • Northwestern University
  • Purdue University
  • University of California, Davis
  • University of California, San Diego
  • University of California, Santa Barbara
  • University of Chicago 

BES Staff Contact

Michael Pechan, Matthias Graf