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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Hongbin Sun
- Kyle Kelley
- Vlastimil Kunc
- Ahmed Hassen
- Anton Ievlev
- Arpan Biswas
- Dan Coughlin
- Gerd Duscher
- Ilias Belharouak
- Jim Tobin
- Josh Crabtree
- Kim Sitzlar
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Merlin Theodore
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Ruhul Amin
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Steven Guzorek
- Subhabrata Saha
- Sumner Harris
- Utkarsh Pratiush
- Vipin Kumar
- Vishaldeep Sharma

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 presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

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.

Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.

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.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

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.