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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Kyle Kelley
- Olga S Ovchinnikova
- Andrew F May
- Anton Ievlev
- Arpan Biswas
- Ben Garrison
- Brad Johnson
- Charlie Cook
- Christopher Hershey
- Craig Blue
- Daniel Rasmussen
- Debangshu Mukherjee
- Gerd Duscher
- Hsin Wang
- James Klett
- John Lindahl
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mike Zach
- Nedim Cinbiz
- Neus Domingo Marimon
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumner Harris
- Tony Beard
- Utkarsh Pratiush

Dual-GP addresses limitations in traditional GPBO-driven autonomous experimentation by incorporating an additional surrogate observer and allowing human oversight, this technique improves optimization efficiency via data quality assessment and adaptability to unanticipated exp

The invention introduces a novel, customizable method to create, manipulate, and erase polar topological structures in ferroelectric materials using atomic force microscopy.

Scanning transmission electron microscopes are useful for a variety of applications. Atomic defects in materials are critical for areas such as quantum photonics, magnetic storage, and catalysis.

The technologies provide a system and method of needling of veiled AS4 fabric tape.

A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.

The scanning transmission electron microscope (STEM) provides unprecedented spatial resolution and is critical for many applications, primarily for imaging matter at the atomic and nanoscales and obtaining spectroscopic information at similar length scales.

ORNL will develop an advanced high-performing RTG using a novel radioisotope heat source.

In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.

This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.