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Researcher
- Peeyush Nandwana
- Anees Alnajjar
- Kyle Kelley
- Rama K Vasudevan
- Amit Shyam
- Blane Fillingim
- Brian Post
- Lauren Heinrich
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- An-Ping Li
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- Anton Ievlev
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- Liam Collins
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- Marti Checa Nualart
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Ondrej Dyck
- Peter Wang
- Ryan Dehoff
- Saban Hus
- Sheng Dai
- Steven J Zinkle
- Steven Randolph
- Tim Graening Seibert
- Tomas Grejtak
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Ying Yang
- Yiyu Wang
- Yongtao Liu
- Yutai Kato

The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

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.

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

Distortion in scanning tunneling microscope (STM) images is an unavoidable problem. This technology is an algorithm to identify and correct distorted wavefronts in atomic resolution STM images.

Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.