Machine‐Learning Spectral Indicators of Topology
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- Center for Nanoscale Materials Argonne National Laboratory Lemont IL 60439 USA, Quantum Measurement Group Massachusetts Institute of Technology Cambridge MA 02139 USA, Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Physics University of Pennsylvania Philadelphia PA 19104 USA, John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA
- John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA, Department of Physics Princeton University Princeton NJ 08544 USA, Donostia International Physics Center P. Manuel de Lardizabal 4 Donostia‐San Sebastian 20018 Spain, IKERBASQUE Basque Foundation for Science Plaza Euskadi 5 Bilbao 48009 Spain
- John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA, Department of Physics Princeton University Princeton NJ 08544 USA
- Center for Nanoscale Materials Argonne National Laboratory Lemont IL 60439 USA, Quantum Measurement Group Massachusetts Institute of Technology Cambridge MA 02139 USA, IKERBASQUE Basque Foundation for Science Plaza Euskadi 5 Bilbao 48009 Spain, Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA, Advanced Photon Source Argonne National Laboratory Lemont IL 60439 USA
- Center for Nanoscale Materials Argonne National Laboratory Lemont IL 60439 USA, Quantum Measurement Group Massachusetts Institute of Technology Cambridge MA 02139 USA, Department of Physics University of Pennsylvania Philadelphia PA 19104 USA, John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA
- Department of Physics University of Pennsylvania Philadelphia PA 19104 USA, John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA, Advanced Photon Source Argonne National Laboratory Lemont IL 60439 USA, Department of Mathematics University of Wisconsin–Madison Madison WI 53706 USA, Computational Research Division Lawrence Berkeley Laboratory Berkeley CA 94720 USA
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 F 1 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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- European Research Council (ERC); National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
- Grant/Contract Number:
- AC02-05CH11231; AC02-06CH11357; SC0020148; SC0021940
- OSTI ID:
- 1895495
- Journal Information:
- Advanced Materials, Journal Name: Advanced Materials Journal Issue: 49 Vol. 34; ISSN 0935-9648
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
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