Journal
ADVANCED MATERIALS
Volume 34, Issue 49, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202204113
Keywords
machine learning; topological materials; X-ray absorption spectroscopy
Categories
Funding
- National Science Foundation GRFP [DGE-1745303]
- U.S. Department of Energy (DOE), Office of Science
- Basic Energy Sciences (BES) [DE-SC0021940]
- DOE [DE-SC0020148]
- NSF [DMR-2118448]
- Norman C. Rasmussen Career Development Chair
- Class of 1947 Career Development Chair
- Schmidt DataX Fund at Princeton University
- Schmidt Futures Foundation
- NSF-MRSEC [DMR-2011750]
- European Research Council (ERC) under the European Union [101020833]
- Applied Mathematics Program of the U.S. DOE Office of Science Advanced Scientific Computing Research [DE-AC02-05CH11231]
- U.S. Department of Energy Office of Science User Facility
- U.S. DOE, Office of Basic Energy Sciences [DE-AC02-06CH11357]
- Laboratory Directed Research and Development (LDRD)
- Office of Science, of the U.S. Department of Energy [DE-AC02-06CH11357]
- DOE Office of Science by Argonne National Laboratory [DE-AC02-06CH11357]
- U.S. Department of Energy (DOE) [DE-SC0020148, DE-SC0021940] Funding Source: U.S. Department of Energy (DOE)
- European Research Council (ERC) [101020833] Funding Source: European Research Council (ERC)
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Topological materials discovery is significant in condensed matter physics, but the experimental determination of materials' topology is challenging. Researchers have used X-ray absorption spectroscopy and neural networks to predict the topological class of materials, potentially leading to the discovery of new topological materials and further understanding of field-driven phenomena.
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.
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