NC State Leads NSF Program to Train Future Leaders at Nexus of Big Data and Materials Science

North Carolina State University and North Carolina Central University are launching a National Science Foundation-funded program to recruit and train researchers in new ways of applying advanced statistical tools to physical science data. In short, they want to create a new generation of scientists who work at the crossroads of big data and materials science.

The five-year program, called Data-Enabled Science and Engineering of Atomic Structure (SEAS), is supported by a Beth Dickey, a professor of materials science and engineering at NC State and the director of SEAS. “Computational techniques have also advanced, giving us unprecedented amounts of data from modeling and simulation. That means we need to develop new, hybrid areas of expertise that allow us to capitalize on these humongous data sets in an efficient and meaningful way.”

“Our goal with SEAS is to develop a cadre of graduate students at the nexus of characterization techniques – such as electron microscopy – and mathematical, statistical and modeling techniques, who can address big data challenges,” Dickey says.

SEAS plans to train at least 15 graduate students per year, starting in the second year of the program. There will also be a total of 12 paid graduate fellows per year, divided between NC State and NC Central.

“These trainees will become champions of this emerging, interdisciplinary field and global leaders in data-driven interdisciplinary STEM research,” Dickey says. “This effort is consistent with NC State’s data science initiative, which aims to advance the ways we understand, manage and make use of data.”

Co-directors of SEAS are Brian Reich, an associate professor of statistics at NC State, and Caesar Jackson, a professor of physics at NC Central.

The SEAS program is part of the NSF’s overarching Research Traineeship Program, which is designed to facilitate the creation of “bold, new and potentially transformative models for STEM graduate training.”

Atom Art: Beauty at the Atomic Scale









“Nature makes some beautiful patterns with atoms.”

That’s what Jim LeBeau wants people to take away from an art exhibit he is helping to curate at the Museum of Life and Science in Durham, N.C that runs from Sept. 20 – Nov. 20, 2016.

“We want to show people that we are now able to actually see atoms, and the orderly way that atoms are arranged in a material,” says LeBeau, an associate professor of materials science and engineering at NC State.

And how those atoms are arranged, the patterns they make, is key to understanding a material’s properties – and how engineers can control those properties.

For example, the image above shows silicon nitride, and highlights how differently atoms can organize in a material depending on how it was manufactured. All silicon nitride is composed of the same atoms, but those atoms can be arranged in very different ways. In this example, the silicon nitride whose pattern is shown in the upper left is much harder than the silicon nitride whose pattern is shown in the lower right.

“Some of my work is supported by the National Science Foundation, and NSF thinks it is important to share our discoveries with the public,” LeBeau says. “I agree. By partnering with the Museum of Life and Science, we are able to help people of all ages understand how we can glean insights into what materials look like at the most fundamental level.”

“The beauty of these patterns highlights just how amazing nature is. Hopefully, we’ll reach young people who may not have otherwise thought about pursuing science,” LeBeau says. “Who knows? Maybe I’ll see some of them in my classroom one day.”

New Approach to Determining How Atoms Are Arranged In Materials

Researchers from North Carolina State University, the National Institute of Standards and Technology (NIST) and Oak Ridge National Laboratory (ORNL) have developed a novel approach to materials characterization, using Bayesian statistical methods to glean new insights into the structure of materials. The work should inform the development of new materials for use in a variety of applications.

“We want to understand the crystallographic structure of materials – such as where atoms are located in the matrix of a material – so that we have a basis for understanding how that structure affects a material’s performance,” says Jacob Jones, a professor of materials science and engineering at NC State and co-author of a paper on the work. “This is a fundamentally new advance that will help us develop new materials that can be used in everything from electronics and manufacturing to vehicles and nanotechnologies.”

The first step in understanding a material’s crystallographic structure is bombarding a sample of the material with electrons, photons or other subatomic particles, using technology such as the Spallation Neutron Source at ORNL or the Advanced Photon Source at Argonne National Laboratory. Researchers can then measure the angle and energy of these particles as they are scattered by the material.

Then things get really tricky.

Traditionally, the data from these scattering experiments has been analyzed using “least squares fitting” statistical techniques to infer a material’s crystallographic structure. But these techniques are limited; they can tell researchers what a material’s structure is likely to be – but they don’t fully describe the variability or uncertainty within the material’s structure, because they don’t describe the answers using probabilities.

“Least squares is a straightforward technique, but it doesn’t allow us to describe the inferred crystallographic structure in a way that answers the questions that the materials scientists want to ask,” says Alyson Wilson, a professor of statistics at NC State and co-author of the paper. “But we do have other techniques that can help address this challenge, and that’s what we’ve done with this research.”

In reality, the space between atoms isn’t constant – it’s not fixed throughout a sample. And the same is true for every aspect of a material’s structure.

“Understanding that variability, now possible with this new approach, allows us to characterize materials in a new, richer way,” Jones says.

This is where Bayesian statistics comes into play.

“For example, atoms vibrate,” Wilson says. “And the extent of the vibration is controlled by their temperature. Researchers want to know how those vibrations are influenced by temperature for any given material. And Bayesian tools can give us probabilities of these thermal displacements in a material.”

“This approach will allow us to analyze data from a wide variety of materials characterization techniques – all forms of spectroscopy, mass spectrometry, you name it – and more fully characterize all kinds of matter,” Jones says.

“Honestly, it’s very exciting,” adds Jones, who is also the director of NC State’s Analytical Instrumentation Facility, which houses many of these types of instruments.

“We also plan to use these techniques to combine data from different types of experiments, in order to offer even more insights into material structure,” Wilson says.

The paper, “Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis,” is published in the Nature journal Scientific Reports. Lead authors of the paper are Chris Fancher, who is a postdoctoral researcher at NC State, and Zhen Han, a former Ph.D. student at NC State. Co-authors include Igor Levin of NIST; Katharine Page of ORNL; Brian Reich, an associate professor of statistics at NC State; and Ralph Smith, a Distinguished Professor of Mathematics at NC State. The work was done with support from the Kenan Institute for Engineering, Technology and Science at NC State, the Eastman Chemical Company-University Engagement Fund at NC State, the National Science Foundation under grant DMR-1445926, and the U.S. Department of Energy’s Office of Science under contract number DE-AC02-06CH11357.