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arXiv · 2607.15763

Machine-learning test of the single-ion model for $dd$ excitations in cuprates

Maryia Zinouyeva·Leonardo Martinelli·Riccardo Arpaia·Nicholas B. Brookes·Daniele Di Castro·Kurt Kummer·Floriana Lombardi·Giacomo Merzoni·Francesco Rosa·Alessandro Tarasio·Enrico Tassi·Flora Yakhou-Harris·Ezio Puppin·Marco Moretti Sala·Giacomo Ghiringhelli
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Abstract

We investigate $dd$ excitations in Resonant Inelastic X-ray Scattering spectra of YBa$_2$Cu$_3$O$_6$ and La$_2$CuO$_4$ using the local single-ion model. The data are analyzed by conventional global fitting and by a convolutional neural network trained within the same theoretical framework. For YBa$_2$Cu$_3$O$_6$, the excited state energies obtained with the two methods coincide, leading to the $xy$, $3z^2-r^2$, $xz/yz$ sequence for increasing energy. This result validates the use of machine learning tools for the analysis of RIXS spectra dominated by $dd$ excitations. By contrast, for La$_2$CuO$_4$, the two methods do not converge to a single solution, revealing the limitations of the single-ion model in describing $dd$ excitations in cuprates and pointing to the role of additional contributions beyond a purely local picture in shaping high-energy excitations.

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Maryia Zinouyeva, Leonardo Martinelli, Riccardo Arpaia, Nicholas B. Brookes, Daniele Di Castro, Kurt Kummer, Floriana Lombardi, Giacomo Merzoni, Francesco Rosa, Alessandro Tarasio, Enrico Tassi, Flora Yakhou-Harris, Ezio Puppin, Marco Moretti Sala, Giacomo Ghiringhelli. 2026-07-17. Machine-learning test of the single-ion model for $dd$ excitations in cuprates. https://arxiv.org/abs/2607.15763

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