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Researcher
- Rama K Vasudevan
- Ryan Dehoff
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Kyle Kelley
- Michael Kirka
- Vincent Paquit
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
- Alice Perrin
- Amir K Ziabari
- Amit Shyam
- Andres Marquez Rossy
- Anton Ievlev
- Arpan Biswas
- Blane Fillingim
- Brian Post
- Christopher Ledford
- Clay Leach
- David Nuttall
- Gerd Duscher
- James Haley
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Philip Bingham
- Rangasayee Kannan
- Roger G Miller
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Singanallur Venkatakrishnan
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Utkarsh Pratiush
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Yan-Ru Lin
- 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

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.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.

In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.