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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Yong Chae Lim
- Zhili Feng
- Jian Chen
- Kyle Kelley
- Rangasayee Kannan
- Soydan Ozcan
- Wei Zhang
- Xianhui Zhao
- Adam Stevens
- Alex Roschli
- Anton Ievlev
- Arpan Biswas
- Brian Post
- Bryan Lim
- Dali Wang
- Erin Webb
- Evin Carter
- Gerd Duscher
- Halil Tekinalp
- Jeremy Malmstead
- Jiheon Jun
- Kitty K Mccracken
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Mengdawn Cheng
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Paula Cable-Dunlap
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sanjita Wasti
- Sarah Graham
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Tomas Grejtak
- Tyler Smith
- Utkarsh Pratiush
- William Peter
- Yiyu Wang
- Yukinori Yamamoto

We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.

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

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

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

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.

The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.

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.

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.