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. A schematic illustrating how a neural network is used to match data from scanning tunneling microscopy to a theoretical hypothesis.
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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.
Co-authors include J.C. Séamus Davis , Cornell’s James Gilbert White Distinguished Professor in the Physical Sciences, an innovator in STM-driven studies.
The research yielded new insights into how electrons interact – and showed how machine learning can be used to drive further discovery in experimental quantum physics.
At the subatomic scale, a given sample will include trillion trillions of electrons interacting with each other and the surrounding infrastructure. Electrons’ behavior is determined partly by the tension between their two competing tendencies: to move around, associated with kinetic energy; and to stay far away from each other, associated with repulsive interaction energy.
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In this study, Kim and collaborators set out to discover which of these tendencies is more important in a high-temperature superconductive material.
Using STM, electrons tunnel through a vacuum between the conducting tip of the microscope and the surface of the sample being examined, providing detailed information about the electrons’ behavior.
“The problem is, when you take data like that and record it, you get image-like data, but it’s not a natural image, like an apple or a pear,” Kim said. The data generated by the instrument is more like a pattern, she said, and about 10,000 times more complicated than a traditional measurement curve. “We don’t have a good tool to study those kinds of data sets.”[…]
read more – copyright by news.cornell.edu
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. A schematic illustrating how a neural network is used to match data from scanning tunneling microscopy to a theoretical hypothesis.
copyright by news.cornell.edu
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.
Co-authors include J.C. Séamus Davis , Cornell’s James Gilbert White Distinguished Professor in the Physical Sciences, an innovator in STM-driven studies.
The research yielded new insights into how electrons interact – and showed how machine learning can be used to drive further discovery in experimental quantum physics.
At the subatomic scale, a given sample will include trillion trillions of electrons interacting with each other and the surrounding infrastructure. Electrons’ behavior is determined partly by the tension between their two competing tendencies: to move around, associated with kinetic energy; and to stay far away from each other, associated with repulsive interaction energy.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
In this study, Kim and collaborators set out to discover which of these tendencies is more important in a high-temperature superconductive material.
Using STM, electrons tunnel through a vacuum between the conducting tip of the microscope and the surface of the sample being examined, providing detailed information about the electrons’ behavior.
“The problem is, when you take data like that and record it, you get image-like data, but it’s not a natural image, like an apple or a pear,” Kim said. The data generated by the instrument is more like a pattern, she said, and about 10,000 times more complicated than a traditional measurement curve. “We don’t have a good tool to study those kinds of data sets.”[…]
read more – copyright by news.cornell.edu
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