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Kevin Garrity (Fed)

Research Physicist, Materials Structure and Data Group

I am a computational materials scientist / theoretical condensed matter physicist. My research consists most frequently of using first principles computational tools, such as density functional theory (DFT), to understand and predict the properties of useful new materials. These calculations are used to both lead experiments into useful new areas and help understand  previous experimental findings.

Within that framework, I have worked on many different materials types and materials properties, including surfaces and interfaces, ferroelectrics, topological insulators, thermoelectrics, catalysts, and semiconductors, as well as on computational techniques like pseudopotentials.

Some of my work at NIST has focused on using high-throughput computational techniques, where a large database of materials is screened computationally to assess the suitability of individual materials for a particular application. First principles calculations are ideal for an initial screening procedure, as they do not require experimental input and can potentially be applied to hundreds or thousands of materials automatically. After the initial computationally efficient screening, the most promising materials can be subjected to further theoretical or experimental consideration.

In addition, I use modeling of large databases to prediction new materials properties, for example with advanced tight-binding.

See also: ,


Computational scanning tunneling microscope image database

Kamal Choudhary, Kevin Garrity, Charles Camp, Sergei Kalinin, Rama Vasudevan, Maxim Ziatdinov, Francesca Tavazza
We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D)
Created December 10, 2018, Updated December 8, 2022