Harnessing machine learning to analyze quantum material

Electrons and their behavior pose fascinating questions for quantum physicists, and recent innovations in sources, instruments and facilities allow researchers to potentially access even more of the information encoded in quantum materials. However, these research innovations are producing unprecedented--and until now, indecipherable--volumes of data. "The information content in a piece of material can quickly exceed the total information content in the Library of Congress, which is about 20 terabytes," said Eun-Ah Kim, professor of physics in the College of Arts and Sciences, who is at the forefront of both quantum materials research and harnessing the power of machine learning to analyze data from quantum material experiments. "The limited capacity of the traditional mode of analysis--largely manual--is quickly becoming the critical bottleneck," Kim said. A group led by Kim has successfully used a machine learning technique developed with Cornell computer scientists to analyze massive amounts of data from the quantum metal Cd2Re2O7, settling a debate about this particular material and setting the stage for future machine learning aided insight into new phases of mater.