Machine learning spectral indicators of topology

Topological materials discovery has emerged as an important frontier in condensed matter physics due to the exceptional properties arising from nontrivial band topology. Recent theoretical methods based on local and global symmetry indicators have been used to identify several thousand candidate topological materials, yet experimental determination of materials’ topological character often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used characterization technique of materials’ local geometric and electronic structure, as it is sensitive to the symmetry and local chemical environment of constituent atoms; thus, it is a potentially useful encoding of topological character. Here, we study the effectiveness of XAS as a predictor of topology using machine learning methods to disentangle key structural information from the complex spectral features. We discuss the utility of experimental spectra to inform materials’ topology and compare the predictive power of individual absorbing elements.