Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2025-09-10

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Department of Applied Physics

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Language

en

Pages

6

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Nano Letters, Volume 25, issue 36, pp. 13435-13440

Abstract

Extracting the Hamiltonian parameters of nanoscale quantum magnets from experimental measurements is a significant challenge in quantum matter. Here we establish a machine learning strategy to extract the parameters of a spin Hamiltonian from inelastic spectroscopy with scanning tunneling microscopy, and we demonstrate this methodology experimentally with an artificial nanoscale molecular magnet based on cobalt phthalocyanine (CoPC) molecules on NbSe2. We show that this technique allows us to extract the Hamiltonian parameters of a quantum magnet from the differential conductance, including the substrate-induced spatial variation of the exchange couplings. Our methodology leverages a machine learning algorithm trained on exact quantum many-body simulations with tensor networks of finite quantum magnets, leading to a methodology that predicts the Hamiltonian parameters of CoPC quantum magnets of arbitrary size. Our results demonstrate how quantum many-body methods and machine learning enable us to learn a microscopic description of nanoscale quantum many-body systems with scanning tunneling spectroscopy.

Description

| openaire: EC/HE/101142364/EU//GETREAL | openaire: EC/HE/101170477/EU//ULTRATWISTROICS

Keywords

many-body physics, Hamiltonian learning, machine learning, scanning tunneling microscopy, molecular quantum magnets

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DOI

10.1021/acs.nanolett.5c02502

Citation

Koch, R, Drost, R, Liljeroth, P & Lado, J 2025, 'Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet', Nano Letters, vol. 25, no. 36, pp. 13435-13440. https://doi.org/10.1021/acs.nanolett.5c02502

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https://urn.fi/URN:NBN:fi:aalto-202508276843

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[article-cris] Perustieteiden korkeakoulu / SCI