Machine‐Learning Spectral Indicators of Topology. Issue 49 (31st October 2022)

• Record Type: Journal Article
• Authors: Andrejevic, Nina; Andrejevic, Jovana; Bernevig, B. Andrei; Regnault, Nicolas; Han, Fei; Fabbris, Gilberto; Nguyen, Thanh; Drucker, Nathan C.; Rycroft, Chris H.; Li, Mingda
• Is Part Of: Advanced materials. Volume 34:Issue 49(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-31
• Subjects: machine learning -- topological materials -- X‐ray absorption spectroscopy; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202204113
• Languages: English
• ISSNs: 0935-9648

Abstract

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. Abstract : A data‐driven classifier of band topology for a database of over 10 000 compounds using X‐ray absorption spectroscopy (XAS) features as input, achieving over 90% accuracy is developed. By leveraging the simplicity and versatility of XAS, this classifier has the potential to accelerate the discovery and screening of a much broader category of candidate topological materials.