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Smith","Wilfred F. van Gunsteren","Niels Hansen" ], "authors": [ {"first" : "Lorna J.", "last" : "Smith"}, {"first" : "Wilfred F.", "last" : "van Gunsteren"}, {"first" : "Niels", "last" : "Hansen"} ], "volume": "129","number": "51","pages": "13174\u201313187", "issn" : "1520-5207", "doi" : "10.1021/acs.jpcb.5c06511", "bibtexKey": "Smith_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/25ec7e64f50eb249d074d379c00ba1339/niels_hansen", "tags" : [ "myown","pn3-8","pn3","pn3-8send:unibiblio","peerreviewed","exc2075" ], "intraHash" : "5ec7e64f50eb249d074d379c00ba1339", "interHash" : "2c32297415f436e8b9606a1e1f2975e3", "label" : "Comment on ``Advancing material property prediction: using physics-informed machine learning models for viscosity''", "user" : "niels_hansen", "description" : "", "date" : "2026-01-08 17:49:10", "changeDate" : "2026-01-08 17:49:10", "count" : 5, "pub-type": "article", "journal": "Journal of Cheminformatics", "year": "2025", "url": "https://doi.org/10.1186/s13321-025-01070-9", "author": [ "Maximilian Fleck","Samir Darouich","Marcelle B. M. Spera","Niels Hansen" ], "authors": [ {"first" : "Maximilian", "last" : "Fleck"}, {"first" : "Samir", "last" : "Darouich"}, {"first" : "Marcelle B. M.", "last" : "Spera"}, {"first" : "Niels", "last" : "Hansen"} ], "volume": "17","number": "1","pages": "131","abstract": "When data availability is limited, the prediction of properties through purely data-driven machine learning (ML) is challenging. Integrating physically-based modeling techniques into ML methods may lead to better performance. In a recent work by Chew et al. (``Advancing material property prediction: using physics-informed machine learning models for viscosity'') descriptors from classical molecular dynamics (MD) simulations were included into a quantitative structure--property relationship to accurately predict temperature-dependent viscosity of pure liquids. Through feature importance analysis, the authors found that heat of vaporization was the most relevant descriptor for the prediction of viscosity. In this comment, we would like to discuss the physical origin of this finding by referring to Eyring's rate theory, and develop an alternative modeling approach using a thermodynamic-based architecture that requires less input data.", "issn" : "1758-2946", "doi" : "10.1186/s13321-025-01070-9", "bibtexKey": "Fleck2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/21f6ec727ca2374f850061a49915e4fc6/niels_hansen", "tags" : [ "myown","peerreviewed","exc2075" ], "intraHash" : "1f6ec727ca2374f850061a49915e4fc6", "interHash" : "5038d6c5566aaa3877f0a447e6707967", "label" : "Parametrized, Transferable Classical Density Functional Theory for Alkane/Alkene Separation in Cationic Zeolites", "user" : "niels_hansen", "description" : "", "date" : "2026-01-08 17:47:40", "changeDate" : "2026-01-08 17:47:40", "count" : 4, "pub-type": "article", "journal": "The Journal of Physical Chemistry B","publisher":"American Chemical Society (ACS)", "year": "2025", "url": "http://dx.doi.org/10.1021/acs.jpcb.5c06005", "author": [ "Tiong Wei Teh","André Kowoll","Gernot Bauer","Rolf Stierle","Joachim Gross","Niels Hansen" ], "authors": [ {"first" : "Tiong Wei", "last" : "Teh"}, {"first" : "André", "last" : "Kowoll"}, {"first" : "Gernot", "last" : "Bauer"}, {"first" : "Rolf", "last" : "Stierle"}, {"first" : "Joachim", "last" : "Gross"}, {"first" : "Niels", "last" : "Hansen"} ], "volume": "129","number": "48","pages": "12551\u201312563", "issn" : "1520-5207", "doi" : "10.1021/acs.jpcb.5c06005", "bibtexKey": "Teh_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cc2eba75b7b34c16b1d6c24d1eeaa813/niels_hansen", "tags" : [ "myown","pn3-8","pn3","pn3-8send:unibiblio","peerreviewed","exc2075" ], "intraHash" : "cc2eba75b7b34c16b1d6c24d1eeaa813", "interHash" : "b4f281b71723e78a16eab22b383cd3ce", "label" : "Generalized DeepONets for viscosity prediction using learned entropy scaling references", "user" : "niels_hansen", "description" : "", "date" : "2026-01-08 17:46:32", "changeDate" : "2026-01-08 17:46:32", "count" : 6, "pub-type": "article", "journal": "Digital Discovery","publisher":"RSC", "year": "2025", "url": "http://dx.doi.org/10.1039/D5DD00179J", "author": [ "Maximiliam Fleck","Marcelle B. 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The resulting architecture can be interpreted as two linked DeepONets with generalization capabilities.", "doi" : "10.1039/D5DD00179J", "bibtexKey": "D5DD00179J" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2571ca45b777948ebb956e1270d687c14/marcooesting", "tags" : [ "pn5-10","myown","ourwork","pn5","peerreviewed","updated","exc2075" ], "intraHash" : "571ca45b777948ebb956e1270d687c14", "interHash" : "b8ace9af129cf666ee87414cfabacab6", "label" : "Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes", "user" : "marcooesting", "description" : "", "date" : "2026-01-01 19:45:58", "changeDate" : "2026-01-01 19:45:58", "count" : 5, "pub-type": "article", "journal": "Journal of the American Statistical Association","publisher":"Informa UK Limited", "year": "2025", "url": "http://dx.doi.org/10.1080/01621459.2025.2529024", "author": [ "Juliette Legrand","Philippe Naveau","Marco Oesting" ], "authors": [ {"first" : "Juliette", 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