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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Yong Chae Lim
- Kyle Kelley
- Rangasayee Kannan
- Zhili Feng
- Adam Stevens
- Alexander Enders
- Alexander I Wiechert
- Anton Ievlev
- Arpan Biswas
- Benjamin Manard
- Brian Post
- Bryan Lim
- Charles F Weber
- Christopher S Blessinger
- Costas Tsouris
- Derek Dwyer
- Gerd Duscher
- Jian Chen
- Jiheon Jun
- Joanna Mcfarlane
- Jonathan Willocks
- Junghyun Bae
- Liam Collins
- Louise G Evans
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Matt Vick
- Mengdawn Cheng
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Paula Cable-Dunlap
- Peeyush Nandwana
- Priyanshi Agrawal
- Richard L. Reed
- Roger G Miller
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Tomas Grejtak
- Utkarsh Pratiush
- Vandana Rallabandi
- Wei Zhang
- William Peter
- Yiyu Wang
- Yukinori Yamamoto

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

High-gradient magnetic filtration (HGMF) is a non-destructive separation technique that captures magnetic constituents from a matrix containing other non-magnetic species. One characteristic that actinide metals share across much of the group is that they are magnetic.

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

The lattice collimator places a grid of shielding material in front of a radiation detector to reduce the effect of background from surrounding materials and to enhance the RPM sensitivity to point sources rather than distributed sources that are commonly associated with Natur

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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.

Pyrolysis evolved gas analysis – mass spectrometry (EGA-MS) and pyrolysis gas chromatography – MS (GC-MS) – are powerful analytical tools for polymer characterization.

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.