Research, publications, people, awards, and laboratory milestones.
NMRL researchers demonstrated a data-guided approach for navigating the nonlinear pulsed-laser-deposition process space of Ga-doped ZnO thin films. The study combines thin-film synthesis with machine learning to identify processing conditions for highly conductive and transparent films.
NMRL researchers are exploring how mesoscopic interference, disorder, nonlinear response, and fading memory can be harnessed for physical reservoir computing—an emerging approach to energy-efficient information processing. The work connects fundamental quantum and mesoscopic transport with material-embedded intelligence.
Our work on TiN thin films investigates how disorder drives the crossover from quantum-coherent to classical electronic transport. The study highlights how controlled disorder can provide a powerful route for understanding and engineering transport in technologically important thin-film materials.
Congratulations to Dr. Gitanjali Mishra on completing her Ph.D. in Materials Science & Engineering. Her research at NMRL spanned electronic thin films, transparent conducting oxides, data-guided materials optimization, and emerging electronic materials.
NMRL is expanding its research at the intersection of functional materials, sensing, memory, and computation. This emerging direction asks how the intrinsic dynamics of materials themselves can perform information-processing functions, reducing the separation between sensing and computation.
A new NMRL educational work presents competing physical length scales as a unifying framework for understanding nanoscale phenomena, connecting fundamental concepts with modern materials and devices.