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Researcher
- Amit Shyam
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Alex Plotkowski
- Kevin M Roccapriore
- Lawrence {Larry} M Anovitz
- Maxim A Ziatdinov
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- Mahshid Ahmadi-Kalinina
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- Olga S Ovchinnikova
- Peeyush Nandwana
- Peng Yang
- Peter Wang
- Rangasayee Kannan
- Roger G Miller
- Sai Krishna Reddy Adapa
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Sunyong Kwon
- Utkarsh Pratiush
- William Peter
- Ying Yang
- 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

CO2 capture by mineral looping, either using calcium or magnesium precursors requires that the materials be calcined after CO2 is captured from the atmosphere. This separates the CO2 for later sequestration and returned the starting material to its original state.

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.

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.

Mineral looping is a promising method for direct air capture of CO2. However, reduction of sorbent reactivity after each loop is likely to be significant problems for mineral looping by MgO.

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