Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Katja Hansen"'
Publikováno v:
Energies, Vol 15, Iss 9, p 3397 (2022)
Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Mi
Externí odkaz:
https://doaj.org/article/83b32f96213347b5a9020d759ddecc65
Autor:
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O Anatole von Lilienfeld
Publikováno v:
New Journal of Physics, Vol 15, Iss 9, p 095003 (2013)
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure–property relation
Externí odkaz:
https://doaj.org/article/353547643bb84effb82e2e37aed65a10
Autor:
Fanran Meng, Andreas Wagner, Alexandre B. Kremer, Daisuke Kanazawa, Jane J. Leung, Peter Goult, Min Guan, Sophie Herrmann, Eveline Speelman, Pim Sauter, Shajeeshan Lingeswaran, Martin M. Stuchtey, Katja Hansen, Eric Masanet, André C. Serrenho, Naoko Ishii, Yasunori Kikuchi, Jonathan M. Cullen
Chemical products, such as plastics, solvents, and fertilizers, are essential for supporting modern lifestyles. Yet, producing, using and disposing of chemicals creates adverse environmental impacts which threaten the industry's license to operate. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96726c53cf3e472afac3dd498ca7365b
https://doi.org/10.26434/chemrxiv-2022-hx17h-v2
https://doi.org/10.26434/chemrxiv-2022-hx17h-v2
Publikováno v:
Energies, 15(9)
Energies; Volume 15; Issue 9; Pages: 3397
Energies; Volume 15; Issue 9; Pages: 3397
Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Mi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b59cf00a73e787555cf1aee1d3f2b21f
https://mediatum.ub.tum.de/1663151
https://mediatum.ub.tum.de/1663151
Publikováno v:
Sustainable Built Environments
Encyclopedia of Sustainability Science and Technology ISBN: 9781493924936
Encyclopedia of Sustainability Science and Technology ISBN: 9781493924936
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60c8251d737a5d585e2836106f0aa364
https://doi.org/10.1007/978-1-0716-0684-1_420
https://doi.org/10.1007/978-1-0716-0684-1_420
Autor:
Manfred Schubert-Zsilavecz, Ramona Steri, Katja Hansen, Oliver Schwarz, Ewgenij Proschak, Gisbert Schneider, Heiko Zettl, Lutz Müller-Kuhrt, Timon Schroeter, Klaus-Robert Müller, Matthias Rupp
Publikováno v:
Bioorganic & Medicinal Chemistry Letters. 20:2920-2923
In previous studies, we identified a truxillic acid derivative as selective activator of the peroxisome proliferator-activated receptor γ, which is a member of the nuclear receptor family and acts as ligand-activated transcription factor of genes in
Autor:
Thomas R. Fox, Timon Schroeter, Jan M. Kriegl, Sebastian Mika, Fabian Rathke, Katja Hansen, Georg Rast
Publikováno v:
Journal of Chemical Information and Modeling. 49:1486-1496
In the present work we develop a predictive QSAR model for the blockade of the hERG channel. Additionally, this specific end point is used as a test scenario to develop and evaluate several techniques for fusing predictions from multiple regression m
Publikováno v:
Snyder, JC; Rupp, M; Hansen, K; Blooston, L; Müller, KR; & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22). doi: 10.1063/1.4834075. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/5rc7m9t2
Snyder, JC; Rupp, M; Hansen, K; Blooston, L; Müller, K-R; & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22). doi: 10.1063/1.4834075. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/0ch480mj
The Journal of chemical physics, vol 139, iss 22
The Journal of Chemical Physics
Snyder, JC; Rupp, M; Hansen, K; Blooston, L; Müller, K-R; & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22). doi: 10.1063/1.4834075. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/0ch480mj
The Journal of chemical physics, vol 139, iss 22
The Journal of Chemical Physics
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately diss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fec5a83766c16e2c5f5a63a8f730f391
http://www.escholarship.org/uc/item/5rc7m9t2
http://www.escholarship.org/uc/item/5rc7m9t2
Autor:
Matthias Rupp, Katja Hansen, O. Anatole von Lilienfeld, Klaus-Robert Müller, Matthias Scheffler, Grégoire Montavon, Siamac Fazli, Alexandre Tkatchenko, Franziska Biegler
Publikováno v:
Journal of Chemical Theory and Computation
The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient app