Decoding the processes governing crystal growth is essential for the continued advance of technology, and is the focus of a National Science Foundation Faculty Early Career Development Award received by Julia Dshemuchadse, assistant professor of materials science and engineering at Cornell.
The prestigious grant, also known as the NSF CAREER Award, supports early-career faculty “who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization,” according to the NSF website.
Dshemuchadse received the award for her proposal “Decoding Crystal Growth and Phase Transformations of Complex Structures With Minimalist Self-Assembly Models,” which will be funded with more than $580,000 over the next five years.
At the heart of computer hardware, electric vehicles, medical devices, and other technologies are the innovative materials they are made from. The crystalline structures of those materials give them their unique characteristics, but exactly how complex crystal structures assemble is not well understood by science.
The goal of the project is to shed light on the self-assembly processes behind the growth of both simple and complex crystal structures. Specifically, Dshemuchadse and her research group will study how different particle attachment patterns depend on the symmetry and complexity of crystal structure type, and how they vary with the chemistry of the system.
“Current models only explain the growth of simple structures and those models break down for complex systems,” Dshemuchadse said. “To truly understand the process of crystal growth, we must gain an understanding of complex structures and how they come together.”
The project will also examine phase transformations of crystals as well as the emergence of complex structures from simple building blocks. Such complex processes are difficult to research with laboratory experiments, so Dshemuchadse will use computer simulations – specifically, molecular dynamics simulations – to model the self-assembly of simple systems into complex crystal structures.
“Large amounts of simulated data can be interpreted with machine-learning approaches,” Dshemuchadse said, “and we can apply it directly to simulations to automatically recognize if a particle is still in the fluid or if it’s already in the crystal. If it's attaching to the crystal, what is its role? How does it adopt that new role in the solid versus the fluid?”
The findings will contribute to the understanding of crystal growth and help advance technology through the targeted design of new functional materials.
Dshemuchadse will also use the CAREER Award to introduce materials science to populations historically underrepresented in the field. Her research group will host summer workshops for teachers from rural areas of central New York and Wikipedia editing events to improve the representation of certain populations on the platform’s science entries, and teach through the Cornell Prison Education Program.
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