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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