[{"intvolume":"       162","article_type":"original","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"first_name":"Yair","last_name":"Davidson","full_name":"Davidson, Yair"},{"full_name":"Philipp, Aviad","last_name":"Philipp","first_name":"Aviad"},{"first_name":"Sabyasachi","last_name":"Chakraborty","full_name":"Chakraborty, Sabyasachi"},{"last_name":"Bronstein","first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","orcid":"0000-0001-9699-8730"},{"full_name":"Gershoni-Poranne, Renana","last_name":"Gershoni-Poranne","first_name":"Renana"}],"has_accepted_license":"1","oa_version":"Published Version","volume":162,"file":[{"success":1,"file_id":"19606","date_updated":"2025-04-22T09:27:43Z","relation":"main_file","file_size":7812182,"checksum":"20a31a4c506b52de863bab7d3ff989ef","creator":"dernst","file_name":"2025_JourChemicalPhysics_Davidson.pdf","date_created":"2025-04-22T09:27:43Z","access_level":"open_access","content_type":"application/pdf"}],"file_date_updated":"2025-04-22T09:27:43Z","project":[{"_id":"92f4a086-16d5-11f0-9cad-c929f5c58b0c","grant_number":"863839","name":"Acoustics-based drone navigation and interaction"}],"publication_status":"published","scopus_import":"1","related_material":{"link":[{"relation":"software","url":"https://gitlab.com/porannegroup/magnetic_locality"}]},"department":[{"_id":"AlBr"}],"isi":1,"OA_type":"hybrid","quality_controlled":"1","day":"14","ddc":["000"],"year":"2025","_id":"19595","publication":"Journal of Chemical Physics","citation":{"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>","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>.","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>.","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.","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.","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>","short":"Y. Davidson, A. Philipp, S. Chakraborty, A.M. Bronstein, R. Gershoni-Poranne, Journal of Chemical Physics 162 (2025)."},"oa":1,"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."}],"date_published":"2025-04-14T00:00:00Z","article_number":"144101","month":"04","date_updated":"2025-09-30T12:06:51Z","issue":"14","title":"How local is “local”? Deep learning reveals locality of the induced magnetic field of polycyclic aromatic hydrocarbons","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.","language":[{"iso":"eng"}],"publisher":"AIP Publishing","OA_place":"publisher","type":"journal_article","pmid":1,"doi":"10.1063/5.0257558","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","short":"CC BY-NC (4.0)"},"article_processing_charge":"Yes (in subscription journal)","publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"corr_author":"1","license":"https://creativecommons.org/licenses/by-nc/4.0/","status":"public","external_id":{"pmid":["40197568"],"isi":["001466311300030"]},"date_created":"2025-04-20T22:01:28Z"}]