Physical review. B./Physical review. B

Publisher's Note: Pure density functional for strong correlation and the thermodynamic limit from machine learning [Phys. Rev. B 94 , 245129 (2016)]

Li Li, Thomas E. Baker, Steven R. White, Kieron Burke

Published Mar 24, 2021• 0 citations

Abstract

Received 19 March 2021DOI:https://doi.org/10.1103/PhysRevB.103.119903©2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElectronic structure of atoms & moleculesPhysical SystemsStrongly correlated systemsTechniquesDensity functional theoryDensity matrix renormalization groupMachine learningCondensed Matter, Materials & Applied Physics

DOI: 10.1103/physrevb.103.119903

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Pure density functional for strong correlation and the thermodynamic limit from machine learning

Li Li, Thomas E. Baker, Steven R. White +1 more

arXiv:1609.03705Physical review. B./Physical review. BDec 21, 2016107 citations

Physical review. B./Physical review. B

Publisher's Note: Pure density functional for strong correlation and the thermodynamic limit from machine learning [Phys. Rev. B 94 , 245129 (2016)]

Li Li, Thomas E. Baker, Steven R. White, Kieron Burke

Published Mar 24, 2021• 0 citations

Publisher

Abstract

DOI: 10.1103/physrevb.103.119903

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Pure density functional for strong correlation and the thermodynamic limit from machine learning

Li Li, Thomas E. Baker, Steven R. White +1 more

We use the density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and numbers of atoms. We train and test a machine-learned approximation to $F[n]$ , the universal part of the electronic density functional, to within quantum chemical accuracy. We also develop a data-driven, atom-centered basis set for densities which greatly reduces the computational cost and accurately represents the physical information in the machine-learning calculation. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly stretched bonds without any specific difficulty (unlike all standard DFT approximations), and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.We use the density-matrix renormalization group, applied to a one-dimensional model of continuum Hamiltonians, to accurately solve chains of hydrogen atoms of various separations and numbers of atoms. We train and test a machine-learned approximation to $F[n]$, the universal part of the electronic density functional, to within quantum chemical accuracy. We also develop a data-driven, atom-centered basis set for densities which greatly reduces the computational cost and accurately represents the physical information in the machine-learning calculation. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly stretched bonds without any specific difficulty (unlike all standard DFT approximations), and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.

arXiv:1609.03705Physical review. B./Physical review. BDec 21, 2016107 citations

F[n]

, the universal part of the electronic density functional, to within quantum chemical accuracy. We also develop a data-driven, atom-centered basis set for densities which greatly reduces the computational cost and accurately represents the physical information in the machine-learning calculation. Our calculation (a) bypasses the standard Kohn-Sham approach, avoiding the need to find orbitals, (b) includes the strong correlation of highly stretched bonds without any specific difficulty (unlike all standard DFT approximations), and (c) is so accurate that it can be used to find the energy in the thermodynamic limit to quantum chemical accuracy.