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Researcher
- Ilias Belharouak
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Yong Chae Lim
- Ali Abouimrane
- Kyle Kelley
- Rangasayee Kannan
- Ruhul Amin
- Adam Stevens
- Anton Ievlev
- Arpan Biswas
- Brian Post
- Bryan Lim
- David L Wood III
- Georgios Polyzos
- Gerd Duscher
- Hongbin Sun
- Jaswinder Sharma
- Jiheon Jun
- Junbin Choi
- Liam Collins
- Lu Yu
- Mahshid Ahmadi-Kalinina
- Marm Dixit
- Marti Checa Nualart
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Peeyush Nandwana
- Pradeep Ramuhalli
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Tomas Grejtak
- Utkarsh Pratiush
- William Peter
- Yaocai Bai
- Yiyu Wang
- Yukinori Yamamoto
- Zhijia Du
- 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 ORNL invention addresses the challenge of poor mechanical properties of dry processed electrodes, improves their electrical properties, while improving their electrochemical performance.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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.

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 has developed a new hydrothermal synthesis route to generate high quality battery cathode precursors. The new route offers excellent compositional control, homogenous spherical morphologies, and an ammonia-free co-precipitation process.

The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.

Sodium-ion batteries are a promising candidate to replace lithium-ion batteries for large-scale energy storage system because of their cost and safety benefits.