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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Lawrence {Larry} M Anovitz
- Maxim A Ziatdinov
- Hongbin Sun
- Kyle Kelley
- Andrew G Stack
- Anton Ievlev
- Arpan Biswas
- Gerd Duscher
- Ilias Belharouak
- Juliane Weber
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Peng Yang
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Ruhul Amin
- Sai Krishna Reddy Adapa
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumner Harris
- Utkarsh Pratiush
- 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

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.

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.

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.

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.