Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES

Abstract

Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional theory (DFT) and machine learning (ML) to interpret how boron (B) and nitrogen (N) dopants influence the local electronic environments of graphene. A dataset of 415 DFT-simulated XANES spectra from 91 distinct configurations was analyzed using a region-specific approach by decomposing each spectrum into pi*, sigma*, and post-edge regions. Random forest models trained on these spectral segments identified the pi* region as the most informative for predicting key local electronic descriptors, particularly the Bader charge and mean dopant-carbon bond length. The Bader charge quantifies dopant-induced charge redistribution and local bonding polarity, directly reflecting the degree of electronic perturbation introduced by heteroatom substitution. The enhanced predictive power of the pi* region arises from its strong coupling to the perturbed pi-electron network, which captures these charge-transfer and hybridization effects more effectively than sigma* or post-edge regions. These findings establish Bader charge as a robust and physically meaningful descriptor for quantifying dopant-induced electronic modulation and demonstrate that region-resolved ML analysis of XANES spectra provides a powerful pathway to uncover structure-property relationships in doped graphene and related materials.