Born into a family of architects, Nina Androgenic loved creating drawings of her home and other buildings while a child in Serbia. She and her twin sister shared this passion, along with an appetite for math and science. Over time, these interests converged into a scholarly path that shares some attributes with the family profession, according to Andrejevic, a doctoral candidate in materials science and engineering
Architecture is both a creative and technical field, where you try to optimize features you want for certain kinds of functionality, like the size of a building, or the layout of different rooms in a home, she says. Androgenic’s work in machine learning resembles that of architects, she believes: "We start from an empty site — a mathematical model that has random parameters — and our goal is to train this model, called a neural network, to have the functionality we desire".
Andrejevic is a doctoral advisee of Mingora Li, an assistant professor in the Department of Nuclear Science and Engineering. As a research assistant in Li’s Quantum Measurement Group, she is training her machine-learning models to hunt for new and useful traits in materials. Her work with the lab has landed in such major journals as Nature Communications, Advanced Science, Physical Review Letters, and Nana Letters
One area of special interest to her group is that of topological materials. “These materials are an exotic phase of matter that can transport electrons on the surface without energy loss,” she says. "This makes them highly interesting for making more energy-efficient technologies".
With her sister Jovan, a doctoral candidate in applied physics at Harvard University, Androgenic has developed a method for testing material samples to predict the presence of topological characteristics that is faster and more versatile than other methods.
If the ultimate goal is “producing better-performing, energy-saving technologies,” she says, “we must first know which materials make good candidates for these applications, and that’s something our research can help confirm".
The seeds for this research were planted more than a year ago. "My sister and I always said it would be cool to do a project together, and when Mingora suggested this study of topological materials, it occurred to me that we could make this a formal collaboration,” says Androgenic. The sisters are more similar than most twins, she notes, sharing many academic interests. "Being a twin is a huge part of my life 'and' we work together well, helping each other in areas we don’t understand".
Androgenic’s dissertation work, which encompasses several projects, uses specialized spectroscopic techniques and data analysis, bolstered by machine learning, which can find patterns in vast amounts of data more efficiently than even the most high-throughput computers.
In order to tease out novel and potentially useful properties of materials, researchers must interrogate them at the atomic and quantum scales. Neutron and photon spectroscopic techniques can help capture previously unidentified structures and dynamics, and determine how heat, electric or magnetic fields, and mechanical stress affect materials at the Lilliputian level. The laws governing this realm, where materials do not behave as they might at the macro-scale, are those of quantum mechanics.
You must be logged in to post a comment.