Interpreting X-ray absorption spectra of Vanadyl Phthalocyanines Spin Qubit Candidates using a Machine Learning-Assisted Approach
Jaehyun Lee, Corina M. Urdaniz, Stefano Reale, Kyungju Noh, Denis, Krylov, Andrin Doll, Luciano Colazzo, Yujeong Bae, Christoph Wolf, Fabio, Donati

TL;DR
This study combines X-ray absorption spectroscopy, atomic multiplet calculations, and machine learning to analyze the electronic structure of Vanadyl Phthalocyanines as potential spin qubits, revealing how substrate interactions influence their properties.
Contribution
It introduces a machine learning-assisted approach to interpret XAS spectra and determine electronic structures of VOPc molecules on surfaces, advancing quantum computing material analysis.
Findings
VOPc retains its spin S=1/2 in various configurations
Surface interactions affect the unoccupied 3d orbital energies
Bayesian optimization accelerates multiplet parameter fitting
Abstract
The magnetic dilution of Vanadyl phthalocyanine (VOPc) within the isostructural diamagnetic Titanyl phthalocyanine (TiOPc) affords promising molecular spin qubit platforms for solid-state quantum computing. The development of quantitative methods for determining how the interactions with a supporting substrate impact the electronic structure of the system are fundamental to determine their potential integration in physical devices. In this work we propose a combined approach based on X-ray absorption spectroscopy (XAS), atomic multiplet calculations, and density functional theory (DFT) to investigate the 3d orbital level structure of VOPc on TiOPc/Ag(100). We characterize VOPc in different molecular environments realized by changing the thickness of TiOPc interlayer and adsorption configuration on Ag(100). Depending on the molecular film structure, we find characteristic XAS features…
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Taxonomy
TopicsElectronic and Structural Properties of Oxides · Machine Learning in Materials Science · Surface Chemistry and Catalysis
