Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

Fujinami, Mikito; Kageyama, Ryo; Seino, Junji; Ikabata, Yasuhiro; Nakai, Hiromi

doi:10.1016/j.cplett.2020.137358

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Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

Author(s): Mikito Fujinami , Ryo Kageyama , Junji Seino , Yasuhiro Ikabata , Hiromi Nakai

Publication date Created: June 2020

Publication date (Print): June 2020

Journal: Chemical Physics Letters

Publisher: Elsevier BV

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Orbital-free kinetic-energy density functional theory

Wang (2002)

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Chainer: a next-generation open source framework for deep learning

Tokui (2015)

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Progress on new approaches to old ideas: Orbital-free density functionals

Karasiev (2014)

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Title: Chemical Physics Letters

Abbreviated Title: Chemical Physics Letters

Publisher: Elsevier BV

ISSN (Print): 00092614

Publication date Created: June 2020

Publication date (Print): June 2020

Volume: 748

Page: 137358

Article

DOI: 10.1016/j.cplett.2020.137358

SO-VID: 9d56459d-8a95-492f-9b2e-5339767e1342

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https://www.elsevier.com/tdm/userlicense/1.0/

http://www.elsevier.com/open-access/userlicense/1.0/

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Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

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Orbital-free kinetic-energy density functional theory

Chainer: a next-generation open source framework for deep learning

Progress on new approaches to old ideas: Orbital-free density functionals

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