Computing
Physical reservoir computing
Harnessing mesoscopic dynamics, nonlinear response, and fading memory for energy-efficient information processing.
University of Utah · Materials Science & Engineering
Materials that compute, conduct, convert, and endure.
NMRL integrates thin-film synthesis, quantum and electronic transport, advanced characterization, device engineering, and artificial intelligence to create materials for next-generation technologies.
Thin films → devicesEnd-to-end materials innovation
AI + experimentsData-guided process discovery
Quantum → sustainableResearch across scales
University of UtahSalt Lake City, USA
Research themes
From atomic-scale control to functional systems.
Our work connects fundamental materials physics with manufacturable devices and systems.
Electronics
Oxide and 2D semiconductors
Transparent conductors, p- and n-type oxides, heterostructures, transistors, sensors, and optoelectronic devices.
Discovery
AI-guided synthesis
Machine learning and autonomous workflows for navigating nonlinear thin-film process spaces.
Featured research · 2026
Machine learning navigates nonlinear thin-film growth.
NMRL combines pulsed laser deposition with data-guided process–property reconstruction to identify growth windows that simultaneously deliver high conductivity and optical transparency in Ga-doped ZnO.
Explore AI-guided materials discovery
Laboratory director
Ashutosh Tiwari, Ph.D.
Professor of Materials Science & Engineering and Director of the Nanostructured Materials Research Laboratory. His research spans thin films, oxide electronics, spintronics, thermoelectrics, two-dimensional materials, mesoscopic transport, and AI-enabled materials discovery.
Recent highlights
New work from NMRL.
Data-guided optimization of Ga-doped ZnO thin films
Machine learning was used to identify high-performance regions in a nonlinear pulsed-laser-deposition process space.
Mesoscopic interference dynamics for physical reservoir computing
New work explores structured transient memory and nonlinear computation in a mesoscopic physical system.
Autonomous multi-modal sensing for wood upcycling
A sensing and intelligent feedstock-preparation platform advances value-preserving sustainable manufacturing.