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Researcher
- Adam M Guss
- Rama K Vasudevan
- Josh Michener
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Liangyu Qian
- Maxim A Ziatdinov
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- Halil Tekinalp
- Ilenne Del Valle Kessra
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- Jeremy Malmstead
- Jerry Parks
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- Joanna Tannous
- Kitty K Mccracken
- Kyle Davis
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Mengdawn Cheng
- Nandhini Ashok
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Paul Abraham
- Paula Cable-Dunlap
- Sai Mani Prudhvi Valleti
- Sanjita Wasti
- Stephen Jesse
- Sumner Harris
- Tyler Smith
- Utkarsh Pratiush
- Vincent Paquit
- Wei Zhang
- William Alexander
- Yang Liu
- Yasemin Kaygusuz
- Zhili Feng

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.

We have developed an aerosol sampling technique to enable collection of trace materials such as actinides in the atmosphere.

ORNL has developed bacterial strains that can utilize a common plastic co-monomer as a feedstock. This will help enable modern, petroleum-derived plastics to be converted into value-added chemicals.

Direct-acting antivirals are needed to combat coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).

Due to a genes unique nucleotide sequences acquired through horizontal gene transfer, the gene has a transcriptional repressor activity and innate enzymatic role.

We have developed bacterial strains that can convert sustainable feedstocks and waste feedstocks into chemical precursors for next generation plastics.

ORNL has identified a panel of novel nylon hydrolases with varied substrate and product selectivity.

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