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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Alex Plotkowski
- Amit Shyam
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Alexey Serov
- James A Haynes
- Jaswinder Sharma
- Kyle Kelley
- Sumit Bahl
- Xiang Lyu
- Alice Perrin
- Amit K Naskar
- Andres Marquez Rossy
- Anton Ievlev
- Arpan Biswas
- Beth L Armstrong
- Gabriel Veith
- Georgios Polyzos
- Gerd Duscher
- Gerry Knapp
- Holly Humphrey
- James Szybist
- Jonathan Willocks
- Jovid Rakhmonov
- Junbin Choi
- Khryslyn G Araño
- Liam Collins
- Logan Kearney
- Mahshid Ahmadi-Kalinina
- Marm Dixit
- Marti Checa Nualart
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Neus Domingo Marimon
- Nicholas Richter
- Nihal Kanbargi
- Olga S Ovchinnikova
- Peeyush Nandwana
- Ritu Sahore
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumner Harris
- Sunyong Kwon
- Todd Toops
- Utkarsh Pratiush
- Ying Yang

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

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

An electrochemical cell has been specifically designed to maximize CO2 release from the seawater while also not changing the pH of the seawater before returning to the sea.

The ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.

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

Hydrogen is in great demand, but production relies heavily on hydrocarbons utilization. This process contributes greenhouse gases release into the atmosphere.

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.

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.