Machine learning detects quantum electronic state
While electrons such as gold and silver tend not to interact, thereby making those materials good conductors, at the quantum-level and in such materials as Mott insulators and high-temperature superconductors the electrons interact strongly and in ways that are not well understood.
The researchers created a suite of 80 artificial neural networks and trained them to recognize different forms of electrons in an archive of experimentally derived electron quantum material (EQM) image arrays from carrier-doped copper oxide Mott insulators.
The researchers claim the technique has discovered a state they call Vestigial Nematic State (VNS) that was previously only theoretical.
The lead author of a paper on the technique published in Nature is Professor Séamus Davis, of the University of Oxford. He said: “I have focused on visualisation of electrons at atomic level. Twenty years ago we developed a microscope that could see directly where all electrons are in the quantum materials, and how the function. In this new collaboration with Professors Eun-Ah Kim – Cornell – and E. Kathami – San Jose State – we fed an electronic image archive gathered over about 20 years – thousands s of electronic structure images – into these artificial neural networks. To my amazement it actually worked! The Vestigial Nematic State had been predicted by theorists but there was no experimental evidence. It was thrilling to see how the new machine learning technique discovered it hiding in plain sight.”
By fusing machine learning with quantum matter visualization the scientists believe that it will accelerate quantum material advances, especially in the area of high temperature superconductivity and the quest for room temperature quantum computers.
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