Overcoming the Barrier of Orbital-Free Density Functional Theory for
Molecular Systems Using Deep Learning

v1v2 (latest)

Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning

28 September 2023

Siyuan Liu

ArXiv (abs)PDF HTML

Papers citing "Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning"

5 / 5 papers shown

Title
Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao Eike Eberhard Stephan Günnemann 82 3 0 10 Oct 2024
KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory Roman Remme Tobias Kaczun Maximilian Scheurer A. Dreuw Fred Hamprecht 63 11 0 08 May 2023
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory Yixiao Chen Linfeng Zhang Han Wang E. Weinan 55 72 0 01 Aug 2020
Machine Learning of coarse-grained Molecular Dynamics Force Fields Jiang Wang Simon Olsson C. Wehmeyer Adria Pérez Nicholas E. Charron Gianni De Fabritiis Frank Noe C. Clementi AI4CE 38 405 0 04 Dec 2018
By-passing the Kohn-Sham equations with machine learning Felix Brockherde Leslie Vogt Li Li M. Tuckerman K. Burke K. Müller AI4CE 69 607 0 09 Sep 2016