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Related Concept Videos

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Machine learning in electronic-quantum-matter imaging experiments.

Yi Zhang1, A Mesaros1,2, K Fujita3

  • 1Department of Physics, Cornell University, Ithaca, NY, USA.

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|June 21, 2019
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Summary
This summary is machine-generated.

Machine learning (ML) analyzes complex electronic quantum matter (EQM) images. ANNs discover a hidden four-unit-cell periodic state and a coincident nematic EQM state in copper oxide Mott insulators.

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Area of Science:

  • Condensed matter physics
  • Materials science
  • Artificial intelligence

Background:

  • Traditional scientific methods struggle with large, complex datasets from automated instrumentation.
  • Machine learning (ML) has shown success in analyzing synthetic data for electronic quantum matter (EQM).
  • Applying ML to experimental EQM data, like atomic-scale images, presents a new frontier.

Purpose of the Study:

  • To develop and train artificial neural networks (ANNs) capable of recognizing hidden order in EQM image arrays.
  • To analyze experimental EQM image data from carrier-doped copper oxide Mott insulators using these ANNs.
  • To identify novel electronic states within complex, noisy experimental data.

Main Methods:

  • Development and training of a suite of artificial neural networks (ANNs).
  • Analysis of experimentally derived EQM image arrays using the trained ANNs.
  • Utilizing atomic-scale visualization data of electronic quantum matter.

Main Results:

  • ANNs successfully identified hidden order within complex and noisy experimental EQM image data.
  • Discovery of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state.
  • Identification of a coincident unidirectional nematic EQM state.

Conclusions:

  • ML, specifically ANNs, can effectively analyze complex experimental EQM data to uncover hidden states.
  • The discovered states in copper oxide Mott insulators align with strong-coupling theories of electronic liquid crystals.
  • This approach offers a powerful new methodology for scientific discovery in data-rich fields.