Understanding electrons’ intricate behavior has led to discoveries that transformed society, such as the revolution in computing made possible by the invention of the transistor.
Today, through advances in technology, electron behavior can be studied much more deeply than in the past, potentially enabling scientific breakthroughs as world-changing as the personal computer. However, the data these tools generate are too complex for humans to interpret.
A Cornell-led team has developed a way to use machine learning to analyze the data generated by scanning tunneling microscopy (STM) – a technique that produces subatomic scale images of electronic motions in material surfaces at varying energies, providing information unattainable by any other method.
“Some of those images were taken on materials that have been deemed important and mysterious for two decades,” said Eun-Ah Kim, professor of physics. “You wonder what kinds of secrets are buried in those images. We would like to unlock those secrets.”
Kim is senior author of “Machine Learning in Electronic Quantum Matter Imaging Experiments,” which published in Nature June 19. First authors are Yi Zhang, formerly a postdoctoral researcher in Kim’s lab and now at Peking University in China, and Andrej Mesaros, a former postdoctoral researcher in Kim’s lab now at the Université Paris-Sud in France.