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

Rouven Koch, Robert Drost, Peter Liljeroth, Jose L Lado

Abstract read

In one paragraph

Article in Nano letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rouven KochQuTech and Kavli Institute of Nanoscience, Delft University of Technology, Delft 2628 CJ, The Netherlands.

Robert DrostDepartment of Applied Physics, Aalto University, 02150 Espoo, Finland.

Peter LiljerothDepartment of Applied Physics, Aalto University, 02150 Espoo, Finland.ORCID 0000-0003-1253-8097

Jose L LadoDepartment of Applied Physics, Aalto University, 02150 Espoo, Finland.ORCID 0000-0002-9916-1589

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 NbSe

Indexed as

Hamiltonian learningmachine learningmany-body physicsmolecular quantum magnetsscanning tunneling microscopy

Identifiers

PMID40845365

DOI10.1021/acs.nanolett.5c02502

PMCPMC12426914

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.