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Researcher
- Ali Passian
- 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
- Wei Zhang
- Adam Stevens
- Anton Ievlev
- Arpan Biswas
- Brian Post
- Bryan Lim
- Claire Marvinney
- Dali Wang
- Gerd Duscher
- Harper Jordan
- Jiheon Jun
- Joel Asiamah
- Joel Dawson
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Nance Ericson
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Srikanth Yoginath
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Tomas Grejtak
- Utkarsh Pratiush
- Varisara Tansakul
- 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

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.

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.