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Researcher
- Amit Shyam
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Alex Plotkowski
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Yong Chae Lim
- Zhili Feng
- James A Haynes
- Jian Chen
- Kyle Kelley
- Peeyush Nandwana
- Rangasayee Kannan
- Ryan Dehoff
- Sumit Bahl
- Wei Zhang
- Adam Stevens
- Alice Perrin
- Andres Marquez Rossy
- Anton Ievlev
- Arpan Biswas
- Brian Post
- Bryan Lim
- Christopher Fancher
- Dali Wang
- Dean T Pierce
- Gerd Duscher
- Gerry Knapp
- Gordon Robertson
- Jay Reynolds
- Jeff Brookins
- Jiheon Jun
- Jovid Rakhmonov
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Neus Domingo Marimon
- Nicholas Richter
- Olga S Ovchinnikova
- Peter Wang
- Priyanshi Agrawal
- Roger G Miller
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Sunyong Kwon
- Tomas Grejtak
- Utkarsh Pratiush
- William Peter
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

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

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

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.

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.

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

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.