Stephen Jesse

Distinguished Scientist and Section Head for the Nanomaterials Characterization Section at the Center for Nanophase Materials Science

Oak Ridge National Laboratory

Stephen Jesse featured image

Stephen Jesse is a distinguished scientist at Oak Ridge National Laboratory and Section Head for the Nanomaterials Characterization Section at the Center for Nanophase Materials Science. His research involves directing, studying, and utilizing nano and atomic scale transformations to enhance understanding of material behavior and to create new materials and devices based on emerging functionalities. Central to this is developing novel scanning probe and scanning electron microscopy techniques to control and modify materials at the nano and atomic scales. This work is combined with developing large scale data analytics of high-dimensional multi-spectral information for functional imaging.

 

Presentation Title:

Providing Capabilities and Expertise for Quantum Information Science Research at the Center for Nanophase Materials Sciences

Presentation Abstract:

The Center for Nanophase Materials Sciences (CNMS), one of five national nanoscience user facilities, provides cutting-edge capabilities in theory, synthesis/fabrication, and characterization for quantum information science (QIS). This presentation surveys instrumentation and instrumentation for imaging and manipulating matter from atomic to mesoscopic scales, with platforms augmented for optical, phonon, magnetic, and electronic probes down to cryogenic temperatures thus enabling discovery, control, and readout of quantum defects, excitations, and correlated phases. CNMS offers thin-film and 2D materials growth tightly integrated with cleanroom nanofabrication for device-level realization. Users can also access a strong theory team and computing infrastructure for rapid, predictive modeling that guides experiments. Finally, I will highlight how AI and experimental automation are embedded across the center to accelerate data-centric discovery, improve reproducibility, and enable closed-loop, autonomous workflows.