Machine‐Learning Spectral Indicators of Topology
Name
Advanced Materials - 2022 - Andrejevic - Machine‐Learning Spectral Indicators of Topology.pdf
Description
Published version
Size
2.47 MB
Format
Adobe PDF
Checksum (MD5)
297c81f12499600d05c413d3e8e57135
Author(s)
Andrejevic, Nina
Andrejevic, Jovana
Bernevig, B Andrei
Regnault, Nicolas
Han, Fei
Fabbris, Gilberto
Nguyen, Thanh
Drucker, Nathan C
Rycroft, Chris H
Li, Mingda
Date Issued
2022
Journal
Advanced Materials
Publisher
Wiley
Citation
Andrejevic, Nina, Andrejevic, Jovana, Bernevig, B Andrei, Regnault, Nicolas, Han, Fei et al. 2022. "Machine‐Learning Spectral Indicators of Topology." Advanced Materials, 34 (49).
Version
Final published version
Abstract
Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms' local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X-ray absorption near-edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F1 scores of 89% and 93% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine-learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/ADMA.202204113
