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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Kyle Kelley
- Alexander I Kolesnikov
- Alexei P Sokolov
- Anton Ievlev
- Arpan Biswas
- Bekki Mills
- Ben LaRiviere
- Gerd Duscher
- John Wenzel
- Keju An
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Mark Loguillo
- Marti Checa Nualart
- Matthew B Stone
- Nance Ericson
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Paul Groth
- Sai Mani Prudhvi Valleti
- Shannon M Mahurin
- Stephen Jesse
- Sumner Harris
- Tao Hong
- Tomonori Saito
- Utkarsh Pratiush
- Victor Fanelli

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.

Neutron scattering experiments cover a large temperature range in which experimenters want to test their samples.

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.

Neutron beams are used around the world to study materials for various purposes.

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.