---
OA_place: publisher
OA_type: hybrid
_id: '19595'
abstract:
- lang: eng
  text: We investigate the locality of magnetic response in polycyclic aromatic molecules
    using a novel deep-learning approach. Our method employs graph neural networks
    (GNNs) with a graph-of-rings representation to predict nucleus independent chemical
    shifts (NICS) in the space around the molecule. We train a series of models, each
    time reducing the size of the largest molecules used in training. The accuracy
    of prediction remains high (MAE < 0.5 ppm), even when training the model only
    on molecules with up to four rings, thus providing strong evidence for the locality
    of magnetic response. To overcome the known problem of generalization of GNNs,
    we implement a k-hop expansion strategy and succeed in achieving accurate predictions
    for molecules with up to 15 rings (almost 4 times the size of the largest training
    example). Our findings have implications for understanding the magnetic response
    in complex molecules and demonstrate a promising approach to overcoming GNN scalability
    limitations. Furthermore, the trained models enable rapid characterization, without
    the need for more expensive DFT calculations.
acknowledgement: The authors express their gratitude to Professor Dr. Peter Chen for
  his continued support. The authors acknowledge the Branco Weiss Fellowship for supporting
  this research as part of a Society in Science grant and the Israel Science Foundation
  for financial support (Grant No. 1745/23 to R.G.-P.). R.G.-P. is a Branco Weiss
  Fellow, a Horev Fellow, and an Alon Scholarship recipient. A.M.B. was supported
  by the ERC StG EARS and the Israeli Science Foundation.
article_number: '144101'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Yair
  full_name: Davidson, Yair
  last_name: Davidson
- first_name: Aviad
  full_name: Philipp, Aviad
  last_name: Philipp
- first_name: Sabyasachi
  full_name: Chakraborty, Sabyasachi
  last_name: Chakraborty
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
- first_name: Renana
  full_name: Gershoni-Poranne, Renana
  last_name: Gershoni-Poranne
citation:
  ama: Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. How
    local is “local”? Deep learning reveals locality of the induced magnetic field
    of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>. 2025;162(14).
    doi:<a href="https://doi.org/10.1063/5.0257558">10.1063/5.0257558</a>
  apa: Davidson, Y., Philipp, A., Chakraborty, S., Bronstein, A. M., &#38; Gershoni-Poranne,
    R. (2025). How local is “local”? Deep learning reveals locality of the induced
    magnetic field of polycyclic aromatic hydrocarbons. <i>Journal of Chemical Physics</i>.
    AIP Publishing. <a href="https://doi.org/10.1063/5.0257558">https://doi.org/10.1063/5.0257558</a>
  chicago: Davidson, Yair, Aviad Philipp, Sabyasachi Chakraborty, Alex M. Bronstein,
    and Renana Gershoni-Poranne. “How Local Is ‘Local’? Deep Learning Reveals Locality
    of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal
    of Chemical Physics</i>. AIP Publishing, 2025. <a href="https://doi.org/10.1063/5.0257558">https://doi.org/10.1063/5.0257558</a>.
  ieee: Y. Davidson, A. Philipp, S. Chakraborty, A. M. Bronstein, and R. Gershoni-Poranne,
    “How local is ‘local’? Deep learning reveals locality of the induced magnetic
    field of polycyclic aromatic hydrocarbons,” <i>Journal of Chemical Physics</i>,
    vol. 162, no. 14. AIP Publishing, 2025.
  ista: Davidson Y, Philipp A, Chakraborty S, Bronstein AM, Gershoni-Poranne R. 2025.
    How local is “local”? Deep learning reveals locality of the induced magnetic field
    of polycyclic aromatic hydrocarbons. Journal of Chemical Physics. 162(14), 144101.
  mla: Davidson, Yair, et al. “How Local Is ‘Local’? Deep Learning Reveals Locality
    of the Induced Magnetic Field of Polycyclic Aromatic Hydrocarbons.” <i>Journal
    of Chemical Physics</i>, vol. 162, no. 14, 144101, AIP Publishing, 2025, doi:<a
    href="https://doi.org/10.1063/5.0257558">10.1063/5.0257558</a>.
  short: Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne,
    Journal of Chemical Physics 162 (2025).
corr_author: '1'
date_created: 2025-04-20T22:01:28Z
date_published: 2025-04-14T00:00:00Z
date_updated: 2025-09-30T12:06:51Z
day: '14'
ddc:
- '000'
department:
- _id: AlBr
doi: 10.1063/5.0257558
external_id:
  isi:
  - '001466311300030'
  pmid:
  - '40197568'
file:
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has_accepted_license: '1'
intvolume: '       162'
isi: 1
issue: '14'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 92f4a086-16d5-11f0-9cad-c929f5c58b0c
  grant_number: '863839'
  name: Acoustics-based drone navigation and interaction
publication: Journal of Chemical Physics
publication_identifier:
  eissn:
  - 1089-7690
  issn:
  - 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://gitlab.com/porannegroup/magnetic_locality
scopus_import: '1'
status: public
title: How local is “local”? Deep learning reveals locality of the induced magnetic
  field of polycyclic aromatic hydrocarbons
tmp:
  image: /images/cc_by_nc.png
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  name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
  short: CC BY-NC (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 162
year: '2025'
...