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Researcher
- Kyle Kelley
- Rama K Vasudevan
- Sergei V Kalinin
- Stephen M Killough
- Alexander I Kolesnikov
- Alexei P Sokolov
- Anton Ievlev
- Bekki Mills
- Bogdan Dryzhakov
- Bryan Maldonado Puente
- Corey Cooke
- Diana E Hun
- John Wenzel
- Keju An
- Kevin M Roccapriore
- Liam Collins
- Mark Loguillo
- Marti Checa Nualart
- Matthew B Stone
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Nolan Hayes
- Olga S Ovchinnikova
- Peter Wang
- Philip Boudreaux
- Ryan Kerekes
- Sally Ghanem
- Shannon M Mahurin
- Stephen Jesse
- Steven Randolph
- Tao Hong
- Tomonori Saito
- Victor Fanelli
- Yongtao Liu

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.

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

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

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.