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Researcher
- Peeyush Nandwana
- Rama K Vasudevan
- Sergei V Kalinin
- Ying Yang
- Yongtao Liu
- Amit Shyam
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Alex Plotkowski
- Alice Perrin
- Blane Fillingim
- Brian Post
- Kyle Kelley
- Lauren Heinrich
- Rangasayee Kannan
- Ryan Dehoff
- Steven J Zinkle
- Sudarsanam Babu
- Thomas Feldhausen
- Yanli Wang
- Yousub Lee
- Yutai Kato
- Andres Marquez Rossy
- Anton Ievlev
- Arpan Biswas
- Bruce A Pint
- Bryan Lim
- Christopher Fancher
- Christopher Ledford
- Costas Tsouris
- David S Parker
- Gerd Duscher
- Gerry Knapp
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- James A Haynes
- Jay Reynolds
- Jeff Brookins
- 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
- Peter Wang
- Radu Custelcean
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumit Bahl
- Sumner Harris
- Sunyong Kwon
- Tim Graening Seibert
- Tomas Grejtak
- Utkarsh Pratiush
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yan-Ru Lin
- Yiyu Wang

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).

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

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.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.