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Area of Science:

  • Condensed matter physics
  • Materials science
  • Quantum chemistry

Background:

  • Topological materials discovery is crucial but experimentally challenging.
  • X-ray absorption spectroscopy (XAS) probes local atomic structure and bonding, key to topology.
  • The theory of topological quantum chemistry (TQC) links material properties to topology.

Purpose of the Study:

  • To develop a machine learning model for predicting topological material classes using XAS data.
  • To overcome experimental challenges in determining material topology.
  • To accelerate the discovery of novel topological materials.

Main Methods:

  • Computed X-ray absorption near-edge structure (XANES) spectra for over 10,000 inorganic materials.
  • Trained a neural network (NN) classifier to predict topological class from XANES signatures.
  • Leveraged the quantitative agreement between experimental and computational XAS.

Main Results:

  • Achieved high F1 scores: 89% for topological and 93% for trivial classes.
  • Demonstrated NN's ability to predict topological class directly from XANES data.
  • Validated the potential of XAS as a topological indicator.

Conclusions:

  • Machine-learning-augmented XAS is a powerful tool for topological materials discovery.
  • The method can identify topological properties in challenging materials like amorphous compounds.
  • Enables in situ studies of field-driven topological phase transitions.