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Researcher
- Peeyush Nandwana
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Ali Riza Ekti
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- Yousub Lee
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- Kunal Mondal
- Liam Collins
- Mahim Mathur
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Mingyan Li
- Mostak Mohammad
- Neus Domingo Marimon
- Nils Stenvig
- Olga S Ovchinnikova
- Omer Onar
- Oscar Martinez
- Ozgur Alaca
- Peter L Fuhr
- Peter Wang
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sam Hollifield
- Stephen Jesse
- Steven J Zinkle
- Sumner Harris
- Tim Graening Seibert
- Tomas Grejtak
- Utkarsh Pratiush
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Yarom Polsky
- Ying Yang
- Yiyu Wang
- Yutai Kato

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

This technology can help to increase number of application areas of Wireless Power Transfer systems. It can be applied to consumer electronics, defense industry, automotive industry etc.

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.

Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.

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.