Topological materials discovery has emerged as an important frontier in condensed matter physics. Recent theoretical approaches based on symmetry indicators and topological quantum chemistry have been used to identify thousands of candidate topological materials, yet 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 environment; thus, it may encode signatures of materials topology, though indirectly. In this work, we show that XAS can potentially uncover materials topology when augmented by machine learning. By labelling computed X-ray absorption near-edge structure (XANES) spectra of over 16,000 inorganic materials with their topological class, we establish a machine learning-based classifier of topology with XANES spectral inputs. Our classifier correctly predicts 81% of topological and 80% of trivial cases, and can achieve 90% and higher accuracy for materials containing certain elements. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine learning-empowered XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials. It can also inform a variety of field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.
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