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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Ying Yang
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Alice Perrin
- Kyle Kelley
- Steven J Zinkle
- Yanli Wang
- Yutai Kato
- Alex Plotkowski
- Amit Shyam
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Bruce A Pint
- Chengyun Hua
- Christopher Ledford
- Costas Tsouris
- Dali Wang
- David S Parker
- Gabor Halasz
- Gerd Duscher
- Gerry Knapp
- Gs Jung
- Gyoung Gug Jang
- James A Haynes
- Jian Chen
- Jiaqiang Yan
- Jong K Keum
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Michael Kirka
- Mina Yoon
- Neus Domingo Marimon
- Nicholas Richter
- Olga S Ovchinnikova
- Patxi Fernandez-Zelaia
- Petro Maksymovych
- Radu Custelcean
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumit Bahl
- Sumner Harris
- Sunyong Kwon
- Tim Graening Seibert
- Utkarsh Pratiush
- Weicheng Zhong
- Wei Tang
- Wei Zhang
- Xiang Chen
- Yan-Ru Lin
- Zhili Feng

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 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.

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

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.

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

When a magnetic field is applied to a type-II superconductor, it penetrates the superconductor in a thin cylindrical line known as a vortex line. Traditional methods to manipulate these vortices are limited in precision and affect a broad area.

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.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.