Multi-Task Graph Neural Network Predictions of Auger-Electron and X-ray Photoelectron Spectroscopy
Abstract
Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.
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BibTeX
@misc{fouda2026multitask,
title = {{Multi-Task Graph Neural Network Predictions of Auger-Electron and X-ray Photoelectron Spectroscopy}},
author = {Adam E. A. Fouda and Patrick Phillips and Phay J. Ho},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16339},
eprint = {2609.16339},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16339},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multi-Task Graph Neural Network Predictions of Auger-Electron and X-ray Photoelectron Spectroscopy
AU - Adam E. A. Fouda
AU - Patrick Phillips
AU - Phay J. Ho
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.16339
UR - https://arxiv.org/abs/2609.16339
AB - Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.
ER -